1. business and industrial

UNIVERSITY OF LJUBLJANA
FACULTY OF ECONOMICS
MASTER’S THESIS
ACCESS TO BANK LOAN OF SMEs IN KOSOVO
Ljubljana, February, 2015
ARLINDA MUSTAFA
AUTHORSHIP STATEMENT
The undersigned Arlinda Mustafa a student at the University of Ljubljana, Faculty of Economics, (hereafter:
FELU), declare that I am the author of the master’s thesis entitled Access to bank loan of SMEs in Kosovo,
written under supervision of PhD. Aljoša Valentinčič.
In accordance with the Copyright and Related Rights Act (Official Gazette of the Republic of Slovenia, Nr.
21/1995 with changes and amendments) I allow the text of my master’s thesis to be published on the FELU
website.
I further declare
 the text of my master’s thesis to be based on the results of my own research;
 the text of my master’s thesis to be language-edited and technically in adherence with the FELU’s Technical
Guidelines for Written Works which means that I
o cited and / or quoted works and opinions of other authors in my master’s thesis in accordance with
the FELU’s Technical Guidelines for Written Works and
o obtained (and referred to in my master’s thesis) all the necessary permits to use the works of other
authors which are entirely (in written or graphical form) used in my text;
 to be aware of the fact that plagiarism (in written or graphical form) is a criminal offence and can be
prosecuted in accordance with the Copyright and Related Rights Act (Official Gazette of the Republic of
Slovenia, Nr. 21/1995 with changes and amendments);
 to be aware of the consequences a proven plagiarism charge based on the submitted master’s thesis could
have for my status at the FELU in accordance with the relevant FELU Rules on Master’s Thesis.
Ljubljana, February 10th, 2015
Author’s signature:
__________________________________
TABLE OF CONTENTS
INTRODUCTION.................................................................................................................... 1
1
DESCRIPTION OF THE PROBLEM ........................................................................... 3
1.1
2
PURPOSE OF THE RESEARCH ......................................................................................... 4
LITERATURE REVIEW ................................................................................................ 5
2.1 VARIABLES .................................................................................................................. 8
2.1.1
Size of business .................................................................................................... 8
2.1.2
Gender .................................................................................................................. 9
2.1.3
Age of firm ......................................................................................................... 10
2.1.4
Education of manager ........................................................................................ 10
2.1.5
Location of SMEs .............................................................................................. 11
2.1.6
Experience of manager ...................................................................................... 11
2.1.7
Form of business organization ........................................................................... 12
2.1.8
Value of assets ................................................................................................... 13
2.1.9
Business plan ..................................................................................................... 14
3
SMALL AND MEDIUM SIZED OF ENTERPRISE.................................................. 14
3.1
3.2
3.3
3.4
3.5
4
BANKS AND FINANCIAL INSTITUTION OF KOSOVO ...................................... 23
4.1
4.2
4.3
4.4
4.5
5
HISTORY OF BANKS’ DEVELOPMENT IN KOSOVO ........................................................ 23
GENERAL CHARACTERISTIC OF BANKING SYSTEM IN KOSOVO ................................... 25
BANKS ROLE IN FRONT OF SMES................................................................................ 28
BANK’S COMPETITION AND CONCENTRATION IN KOSOVO .......................................... 29
MICRO FINANCE INSTITUTIONS (MFI) ........................................................................ 30
METHODOLOGY APPLIED....................................................................................... 32
5.1
6
DEFINITIONS OF SMES ............................................................................................... 14
SMALL AND MEDIUM SIZED OF ENTERPRISE IN KOSOVO ............................................. 16
CONTRIBUTION OF SMES TO ECONOMIC DEVELOPMENT (GDP) OF KOSOVO ............. 17
FACTORS INFLUENCING SMES GROWTH ..................................................................... 18
FINANCIAL ISSUE ASSOCIATED WITH SMES ............................................................... 20
DESCRIPTION OF VARIABLES ...................................................................................... 32
EMPIRICAL FINDINGS .............................................................................................. 36
6.1
6.2
EMPIRICAL CONSIDERATION ...................................................................................... 36
INTERPRETATIONS OF THE RESULTS............................................................................ 42
POLICY RECOMMENDATIONS ...................................................................................... 47
CONCLUSION
REFERENCES ....................................................................................................................... 50
i
APPENDICES
LIST OF FIGURES
Figure 1. Discouraged Borrower .............................................................................................. 22
Figure 2. Profitability Indicators of Eastern Europe Banking Sector ....................................... 26
Figure 3. Logistic Regression ................................................................................................... 37
LIST OF TABLES
Table 1. Classification of Enterprise by Size in EU ................................................................. 15
Table 2. Classification of Enterprise by Size and Employee in Kosovo (2012) ...................... 16
Table 3. Participation of Micro, SMEs Annual Turnover in GDP of Kosovo ......................... 18
Table 4. Number of Financial Institution Operating in Kosovo ............................................... 24
Table 5. Descriptive Statistics of Variable Used in the Logit Regression Model .................... 33
Table 6. One Sample T-test Borrow Loan = 1 and Otherwise = 0 ........................................... 40
Table 7. One Sample T-test Borrow Loan= 1 and Didn’t Apply =0 ........................................ 40
Table 8. One Sample Ttest Borrow Loan =1 and Refused =0 ................................................. 41
Table 9. Empirical Findings from Average Marginal Effect and Logit Model........................ 44
ii
INTRODUCTION
Small and medium size enterprises (hereinafter: SMEs) are known as the engine of
economic growth of a country. Growth of SMEs stimulates development of the economy;
improve income of people, create job opportunity etc. Many economist concluded that
SMEs community plays an important role in countries in transition by driving growth of
market economy (McIntyre, 2001; Dallago (n.d); Schiffer&Weder, 2001; and Wasmus,
2012).
Kosovo is the newest country in Europe, since it proclaimed its independence on 17th of
February 2008.Kosovo is a region located in South-eastern Europe with an area of
10,908m2, population around 1.8 million and GDP per capita € 2,650.00 in 2011, by
Kosovo Agency Statistic (hereinafter: ASK, 2011).During the period from 1989 to 1999
Kosovo has been occupied and were under forced administration of Republic of Serbia.
Since the end of the war with Serbia in 1999, the Republic of Kosovo was administrated by
United Nations until 2008.
The role of small and medium sized enterprises is significantly important as they give a
special contribution in the growth and development of Kosovo’s economy. Moreover, the
role of SMEs has been different during different periods such as before the war, during the
Serb occupation and in post-war Kosovo. The number of SMEs during the Serbian
occupation time was relatively small and access to bank loans was very difficult or even
impossible due to political circumstances in that time. Right after the war in 1999, a new
era begun and a large number of new businesses entered the market and a few banks
launched their operations, creating a better environment and facilitations for business
development. During that period, a number of people got employed and economy as a
whole started to develop.
However, Kosovo is still in transition phase facing high unemployment rates (45%),
economic instability, and payment imbalances with exports dominating, ineffective judicial
system, etc. Kosovo’s GDP still remains low, comparing to other countries in Southeastern Europe is two times less than Albania, and three times less than Bosnia and
Herzegovina (IMF, 2010). The highest unemployment rate is amongst young people,
women and unqualified people (Karadaku, 2012). Having into consideration the
contribution of enterprises on Kosovo’s GDP (43,10 %), the growth of private sector,
particularly SMEs is expected to play an important role on economic growth, on decrease
of the number of unemployed and decrease of poverty which is the most critical problem
faced by Kosovo population by Tax Administration of Kosovo (hereinafter: TAK),2011.
SMEs development mainly dependents on their ability to obtain funds for growing their
activities. However, their growth is hindered due to a number of obstacles such as: sources
of funds, business environment, informal economy and a number of other external or
internal factors. Therefore, facilitating the access to financial sources for businesses in
1
Kosovo, contribute to the growth of existing businesses, creation of new businesses that
will impact the growth of Kosovo’ GDP, increase of the number of employees, increase of
people’s incomes and overall living standard. This may have impact also on mitigation of
migration of qualified people towards developed European countries will decrease, as one
of the major concern for Kosovo in the recent year.
In order to effectively grow and operate this sector, it is necessary to develop the
infrastructure, to create facilitations for doing business, establish an effective judicial
system and reduce informal economy and corruption. These problems occur in most
countries going through transition, especially corruption which is the main negative
occurrence in transition countries. Based on Global level of Corruption Perception Index
(hereinafter: CPI, 2012), Kosovo remains with the highest level of corruption positioned
105th in a list of 174 world countries, followed by Serbia in 80th, Bosnia Herzegovina 72nd,
Macedonia 69th, Croatia 62nd, etc. Corruption in transition countries is an enemy for the
whole country, because it will hinder economic development, lower the GDP, and at worst
it will impact the number of investors by raising fear and suspicion during their decision
making process regarding the investments.
Despite all the above mentioned problems, another determinant that affects mostly the
growth of SMEs is financial sources which in one way stimulate their growth. The number
of financial source in Kosovo is limited. Thus businesses, in order to finance their activities
initially use all their own funds than go to banks to ask for loan, since there is no other
form of financing. Banks or creditors, due to the market risk and unstable business
environment, put different obstacles for business borrowers such as high interest rates,
requirements for collateral, etc. which are often unaffordable for the businesses.
Access to finances is getting increasingly difficult for businesses, especially after the
global financial crises due to many factors such as market risk, business environment, etc.
Whittle (2012, p. 10) argues that due to global financial crises, banks in Kosovo have
tightened lending conditions, lowering the number of non-performing loans (hereinafter:
NPL). However, according to Central Bank of Kosovo (hereinafter: CBK), 2011 credit
numbers have increased through years, even though entrepreneurs continue to see access to
funds or lending conditions as the main obstacle in developing their businesses
(RIINVEST, 2012).
A number of studies were conducted to determine the main factors that are hindering the
growth of SMEs and impacting their access to finance. According to Wasmus (SME
Finance in Kosovo, a tale of Pride and Prejudice, 2012) there are three main issues
regarding barriers from banks or creditors to SMEs’ access to funds: high level of
informality among small and medium sized enterprises, level of required collateral and
enforcement mechanism and lack of diversified portfolio in the financial sector. Thus, he
recommends that businesses, government and CBK do their part in order to lower interest
rates. Businesses or SMEs must be more transparent with regard to the information
2
provided to banks; government should do more on improving the legal framework and
empowerment of judicial system in order to create facilitations for creditors that have to
seize properties from bad borrower, while the CBK should raise the competition in
financial sector in order to bring diversity in the market (Wasmus, 2012).
Besides all the difficulties faced by SMEs in their operation activities and development,
they also face a limited number of financial sources. In Kosovo market, except for loans
provided by banks or microfinance institutions under very unfavorable lending conditions,
there are no other financial sources for SMEs to finance their activities.
Growth of SMEs and their development are of a crucial importance for underdeveloped
countries undergoing transition, with high rate of unemployment and poverty. Recently,
many policymakers and researchers have published their concerns regarding to
identification of factors that are holding back SME sector from developing. Hence, we
have focused on features that may facilitate SMEs’ access to bank loans. Such features
include gender of the business owner, business location, age of the company, business
organization, manager’s qualification, size of the business, business plan, asset value, etc.
These features have been analyzed using an empirical regression analysis called “The
Logit Model”. The probabilistic logistic model is constructed in order to examine the
impact of the different features of businesses in obtaining loans from banks. Data were
collected through interviews conducted by Business Support Centre Kosovo (hereinafter:
BSCK) with representatives of 500 SMEs in seven regions of Kosovo.
The empirical result suggest that some of the features, such as business location, age of the
company operating in the market, pursuant business plan, and size of the company have
higher probability to impact on bank’s decision for granting the loan. Consequently, older
and larger businesses, operating in rural areas and providing promising business plans have
higher probability to borrow loans from banks, while other features, such as manager’s
qualification, assets value and business organization do not have significant impact on the
access of the company to bank loans.
Thus, this paper will put special emphasis on the ability of companies to find sources of
financing, as a key factor for development of SMEs.
1
DESCRIPTION OF THE PROBLEM
Despite the importance role in economic development, SMEs growth continues to be
hampered by a number of obstacles. Unfavorable business environment, high level of
corruption in countries in transition, informal economy, high interest rates charged on
loans, are holding back SMEs from growing their operations. Banks, due to market
imperfection, have often been reluctant on their lending activity. This has limited the
financial sources that would serve the growth of SMEs that depend strongly on funds to
finance their activities (Dalberg, 2011).
3
SMEs in countries in transition usually finance their activity through their internal funds,
borrowing from family or friends, grants, bank loan or other investors. Most small and
medium enterprise start up with their own funds than afterwards they head to banks for
loan, because other eternal source of finance doesn’t exist in the market of Kosovo.
Difficulties arise when the business go to bank to ask for loan, due to various obstacles set
by creditors. Banks require from the SMEs borrower to fulfill a number of criteria if they
want to get access on bank loan. Businesses in order to overcome these obstacles
sometimes present unreliable data to creditors leading to a stricter bank decision-making
process with regard to lending. Banks due to existing of asymmetric information from
start-up business and due to other reason impose unfavorable lending condition for
business such as difficult procedures for loan approval, high interest rate, requirement for
collateral in a value that is nearly twice as much as the value of the loan in order avoid the
riskier borrower (236,436 average value of the loan compared to 518,265 average value of
collateral requirement in euro-BSCK, 2011). In the market of Kosovo exist also a number
of Microfinance Institution with the main activity of providing source of finance to the
poor business and entrepreneurs; however those credits are characterized with high interest
rates which often are unaffordable from business.
Consequently, as results of luck of funds for financing operations and collateral
requirement and since some other obstacles imposed by banks have been impossible to
overcome for a number of business, many start-ups business have failed in their first year
of operation. Moreover it is really important to find if any business characteristic can have
impact on their ability to get easier access to bank loan.
1.1
Purpose of the research
Having into consideration that Kosovo is a country in transition and it faces various
difficulties on development; proper competitiveness, better infrastructure, lower taxes,
lower level of corruption and better terms from banks for business lenders would
contribute a lot to SMEs’ growth and development of country’s economy.
Kosovo Ministry of Trade and Industry (hereinafter: MTI) through SME Support Agency
has drafted the SME Development Strategy 2012-2016 (MTI, 2011), where one of the
main goals is supporting SMEs to access on finance. Even though this is not the only
difficulty SMEs are facing, their growth mainly depends on their ability to access finance
either from internal funds, family or friends, grants from donors or bank loans. In recent
years, due to global financial crises, number of foreign investors has significantly
decreased and banks have tightened their lending activities for businesses, resulting in even
more obstacles for SMEs in obtaining funds to finance their activities. Mostly, SMEs go to
banks and ask for loans only if they have used all their funds (own or family), because of
4
unfavorable credit terms imposed by banks such as: high value of collateral requirement,
difficult procedures, high interest rate for loans, etc.
The conditions for growth of SMEs are getting difficult day by day, despite the important
role that they have on economic development a country. SMEs play a crucial role in
development and growth of economy, therefore it is really important to examine factors
that result in easier access to bank funds for SMEs. Having into consideration that the only
external source for financing SMEs are bank loans, it is important to identify barriers
imposed by banks and try to convert them to facilitations. Because of current lending
conditions imposed by banks, there are large numbers of SMEs that don’t even apply for a
loan. That group of SMEs doesn’t even try to ask for banks loan because they filling that
will be rejected by bankers. These groups of people are known as discouraged borrowers
(Kone & Storey, 2003).
There is also a number of SMEs that may have a potential idea to develop and create job
opportunities, but in lack of financial resources fail to implement their idea in practice.
Moreover, having into consideration their critical role on the development of Kosovo
economy, they continuously are facing different number of obstacles towards finding
source to finance their business activity.
Therefore, the purpose of this study is to investigate and identify if any characteristics of
SMEs can have an influence on bank decision in order to get easier access to bank loans.
Having into consideration all the difficulties faced by SMEs in finding source of finance
for their growth, or obstacles faced by firms to develop their activity, and also being part of
the research report, Entrepreneurship and Small Business Development in Kosovo, for 500
SMEs located in different regions of Kosovo, motivated me to write about this topic. The
thesis will have mainly focus on determining firm’s features that impact SMEs ability for
easier access to finances–banks’ loan which would contribute to their growth or
development.
In order to identify such features, some of the features were taken into consideration
through the empirical regression model and were testify through the Logit model in order
to achieve up to results. After implementation of the empirical regression result we have
interpreted the results which have led us also to our conclusion.
2
LITERATURE REVIEW
This chapter elaborated the impact of firm features on the firm’s ability to get easier bank
loan on different country including transition, in developing and developed countries.
SMEs are seen as the engine of a country due to their contribution to economic
development. Economies in transition as in Kosovo case, are characterized with high level
5
of unemployment, economic instability and informal economy. Further growth of SMEs
would play a key role in creation of job opportunities, further increase of revenues and
overall development of economy. Growth of SMEs is directly linked with many factors
such as: access to finances, competition, corruption, government policies (Govori, 2013).
Ruta (2003, p.20) classified barriers that hinder the operation of SMEs in countries in
transition into formal (taxes and business legislation) and informal (corruption and failure
of law enforcement).
Financing is the main determinant for the growth of SMEs, both in developed and in
developing countries. Bratkowski, Grosfeld and Rostowski (2000, p.3), on their study, put
special emphasis on the problem of financing for SMEs, both in developed and in countries
in transition. They found that de novo private companies are supported reasonably by
credit market; however, a reform or reorganization of the banking sector would have a
large impact on facilitating access to finance for new private companies. SMEs finances
remains the main challenge even in developed countries due to the reasons explained by
IFC-FIEG (2012) that include regulatory framework, finance infrastructure and
government support to expand finances for SMEs.
In a survey conducted by European Commission (EC-Enterprise and Industry, 2011, p.6)
with SMEs from 27 EU countries, it was found out that SMEs from Greece, Estonia and
Slovenia were facing the most problems in accessing finances. They encounter different
barriers on their development and in imperfect market.
Insufficient law enforcement, corruption, fiscal evasion, informal economy also have a
crucial impact on the development of small enterprises. Berger and Udell (2006, p.17) in
their study stated that small enterprises operating in developed countries use external funds
to finance their operation needs, rather than their internal finances. Beck and DemirgucKunt (2006, p.16) argued that SMEs in developed countries have easier access to finances
compared to SMEs in developing countries.
Ayagari, Demirguc-Kunt and Maksimovic (2006, p.2) emphasized on their study that all
obstacles do not have the same impact. From their regression analysis they found out that
political instability, finances and crime, the growth of companies are directly related to the
growth of SMEs.
UK Government Policy Action Team (1999, p. 7) stated that some of the features of
companies that affect access to finance for small business are age of the company, business
structure, experience and track records. The status of the company is also a factor that can
impact the access to finances. Type of business organization can also an impact on easier
access to funds for a company (ex. Limited Liability Companies are seen by bankers as
more favorable compared to individual business. Harhff and Körting(1998, p.6) proved in
their study that companies registered as Limited Liability Companies have easier to access
6
finance than companies with other status, because LLCs present more security in terms of
implementing the project.
Many researchers have written regarding the financial constrains faced by SMEs. SMEs
operating in countries in transition finance their activities by using bank credit as a source
of external finance (Bond, Meghir & Blundell, 1996; Schiantarelli, 1995; Laeven &
Maksimovic, 2004).However, they face different obstacles in borrowing from creditors due
to the difficult procedures set by banks.
Haas, Ferreira and Taci through their study involving 220 banks in 20 countries in
transition (EBRD, 2007) found out that bank features such as ownership and size, do
influence the choice of bank costumers, thus foreign banks are oriented toward lending to
foreign subsidiary companies and mortgage lending, whereas small banks lend more to
SMEs.
In contrary Ahunov, Van Hove and Jegers (2011, p.7), through their study in countries in
transition (Ukraine) found out that in countries with smaller presence of foreign banks,
there is no difference compared to domestic banks, on the interest to finance businesses,
however, with the increased presence of foreign banks (2005-2006) it was noted that
specific number of foreign banks were involved more on financing SMEs.
In their research on behavior of trade credits in emerging economies affected mostly by
financial crises in 1997, found an increase of trade credit at the peak of financial crises,
although it collapsed after the financial crises, resulting in tightening credits for businesses
(Love, Preve & Allende, 2005). Same situation also applies in SEE countries where banks
have imposed a number of barriers for SMEs accessing finances (Hashi & Toqi, 2010).
Banks impose high interest rates for start up and small entrepreneurs and require high
amount of collateral, because insufficient information about them leads to higher risk of
not returning the loan from borrowers. This was confirmed also by Hashi and Toqi (2010,
p. 55) that banks impose such barriers due to existence of asymmetric information, moral
hazard and agency problems. There are also a number of companies that do not even apply
for loans, because of barriers and difficult procedures applied by banks. This group
differently is called the discouraged borrowers-those that don’t apply for a loan, because
they feel that they will be reject by banks (Kon & Storey, 2003).
Han, Fraser and Storey (2008, p.16), using data from small businesses in U.S. about
discouraged borrowers, through their empirical paper found that age, size, and wealth of
the company play an important role on discouragement. Thus, start-up businesses having
less assets feel uncertain to apply for a loan to banks and this way they classify themselves
as part of the group discouraged borrower. Xuegong and Xueyan (2011, p.12) argue that
percentage of discouraged borrowers decreases with the size of the company. Chakravarty
7
and Xiang (2010, p.11) also found out that business features such as age, size, level of
competition and relationship with banks have impact on discouraged borrower group.
As we can see, SMEs’ access to bank loans depends on a number of factors including
collateral, company size, gender, age and location of the company, education and
experience of the manager, etc. SMEs use internal funds as primary financial source for
their activity, and then use from from family or friends before heading to banks for loan
(Howorth 2001; Beck & Kunt, 2006). This is the same with pecking order theory. The
main external financial source for Kosovo SMEs are bank loans, although Petersen and
Rajan (1994, 1995) in their study classified bank loans as the cheapest source of external
financing. Hashi and Toqi (2010, p.4) on their studies of credit rationing and financing
obstacles for companies in Albania, Bosnia and Herzegovina, Montenegro, Rumania,
Bulgaria, Croatia, Macedonia and Serbia argued that in perfect capital market there are no
differences between use of external and internal sources of financing.
Defining obstacles faced by SMEs is really important, because it will facilitate the
operation of entrepreneurs, helps in creation of new jobs and economic development of a
country. Consequently, we will take into consideration all these factors: size of the
company, gender, age of the company, education of the manager, experience of the
manager, type of business organization, location of the company, business plan and the
value of assets to see if they impact company’s ability to get easier access to bank loans.
2.1
Variables
2.1.1 Size of business
Finance theories with regard to bank loans access have shown that SMEs face more
obstacles in getting loans compared to larger companies, both in developed as well as
developing countries (Pandula, 2011).
For the purposes of our study we will mainly focus on the differences in obstacles in
getting loans between small enterprises and medium enterprises. The classification will be
based on the number of employees, thus enterprises with larger number of employees will
be classified as Medium enterprises, while the ones with smaller number will be
considered as small enterprises. Another finding indicates that firms’ size is one of the
main factors that impacts banks’ decision on lending to those companies (Petersen &
Rajan, 1994). Accordingly, the bigger the SME the higher will be chances for company to
get a loan from bank and vice versa, since bigger companies can provide more collateral,
valuable working capital, and assets compared to the smaller enterprises.
Because of background of small enterprises, insufficient information about them, fewer
assets that can serve as collateral, small enterprises are less favorable when it comes to
borrowings from banks, compared to the medium enterprises. Due to aforementioned
8
features of small enterprises and banks’ exposure in front of those borrowers, apply higher
interest rates for them, deteriorating even the position of good borrowers (Krasniqi,2006).
Bank officer based on the cash flow volatility and business structures, small enterprises see
as riskier borrowers (Storey, 2014). Schiffer and Weder (1994, p.30) in their study, based
on the data from over 10,000 companies from private sector in 80 countries, found a
negative relationship between the size of a company and financing, According to them,
small and medium sized enterprises face more difficulties in access to finance than larger
companies in developed countries (Argentina, Brazil and Colombia), whereas in countries
in transition (Poland and Ukraine) surprisingly, these relationships happen to be the other
way around where small companies have less obstacles in financing than medium or larger
companies. Fatkoi and Asah (2011, p.174) in their survey through logistic regression
found that SMEs with over 50 number of employees have easier access to bank loans,
compared to the SMEs that have less than 50 employees.
2.1.2 Gender
Another factor that has shown a significant role on the SMEs’ to access to finances is the
gender of entrepreneurs. A number of studies have shown that female entrepreneurs are
more constrained than male entrepreneurs in terms of difficulties when borrowing from the
bank. Carter in 2001 in his study agreed that business women face more obstacles while
starting a business compared to men. Also this kind of perception exist among people that
women are not as strong as men in leading businesses (Hisrich & Brush, 1987). Often the
woman has been shown as more conservative and prudent to borrowing (Wilson, 2007).
Roper and Scott (2009, p.9) based on GEM (2004) database of start-up businesses
analyzed gender differences in access to finances and explained that women are more
financial constrained compared to men.
On a brainstorming session, organized by Bid Network and implemented by NGO – BSCK
with 300 entrepreneurs (2012, p. 35) from all Kosovo regions with the topic, What are the
challenges of women in business in Kosovo? The conclusion from it was that most
participants responded that they were against women in business by answering that: the
women are not capable to lead a business, men are .Another reason that women are less
favorable to borrow from banks is the lack of collateral for banks, because apartments,
property or land are mainly registered on the name of their husband (men). Smith and
Jackson (2004, p.18) also cited that financing system is more oriented towards men, while
women face different obstacles to get finances for starting up their businesses. In contrary
to the above mentioned statements, the results from regression analysis of, Growth
determinant of micro businesses in Canada (2002) points out that statistically gender has
no role in growing businesses. Irwin and Grayson (2006, p. 9) in their study of SMEs with
regard to the impact of education, gender and ethnic group on access to finances, come to a
surprising result that women get easier access to finances compared to men. Hisrich and
9
Fulop have cited in Bliss (2001) that most common problems faced by women Hungarian
entrepreneurs in starting up their business are family obligation.
2.1.3 Age of firm
Through different studies it was shown that start-up business are more financially
constrained when it comes to borrowing from banks, compare to business with longer
presence in the market. This is a result of insufficient information for new business,
insufficient assets that can serve as collateral in banks, lack of audited financial statements
and they are also considered with higher risk compared to old businesses in the market.
These features make new businesses fail in their first year of operation in Saharan Africa
(Biekpe, 2004).
Older companies will find easier access to finance and vice versa (Beck, Kunt, Laeven &
Maksimovic, 2003).This also happens in developing country (Abor, 2008). Loan cost is
relatively high, because banks charge high interest rates on loans for new businesses, some
of which cannot even afford them. Hall (2004, p. 6) in his works shows a positive relation
between age of the company and long term debt and negative relation between age of the
company and short term debt. Xuegong and Xueyan (2011, p.33) through their log it model
in their study of SMEs’ access to finances in China, concluded that the older the company
the easier will be the access to finances. Voordeckers and Steijuersin in their study of
2,698 SMEs in Belgium (1993-2001), found that age of companies, cash flow and assets
affect significantly SMEs’ ability to get easier access to bank loans. Berger and Udell
(1995, p.365) argued that older SMEs have easier access to bank loans compared to the
younger ones, because of lower asymmetry information.
2.1.4 Education of manager
Education relates to the level of studying that entrepreneurs have gained during their life.
Managers of SMEs in transition economies, mostly in western countries, are seen with less
education and limited access to training programs (Aidis, 2005). Hisrich and Fulop (1994)
also emphasized on Bliss (2001) in their studies about SMEs in countries in transition that
lack of management skills influence on the SMEs ability to get access on bank loan. A
number of studies have shown that another factor that impacts SMEs’ access to finances
can be education of managers. Irwin and Scott (2005, p. 8), based on their research, found
that education plays an important role on bank’s lending decision, because entrepreneurs
with better education have more chances to borrow from banks. Educated entrepreneurs
prepare promising and persuasive business plans and provide more reliable financial
statements that makes them more credible in front of bankers. IFC (2011, p. 6) in a study “
Strengthening access to finance for women owned SMEs in developing countries”,
explains the important role of education of entrepreneurs on finding easier access to
finance.
10
In contrary to this statement, there were different studies mentioning that education of
entrepreneur doesn’t play any important role in young company’s ability to access finances
in Russia (Hartarska & Vega, 2005, p. 26). Also, Vos (2007, p. 4) in his study, spoke about
the negative relationship between education of manager and access to external finances.
According to him, the lower the level of education of the manager, the greater the use of
loans and vice versa. Abdesamed and Wahab (2012) in their study involving 76 SMEs in
Tripoli, Libya, through their regression analysis about the impact of education, experience
of managers and business plan in the ability of a start-up businesses to obtain loans,
concluded that education of managers is not important. Herrington and Wood (2003)
claimed that high rate of failure of SMEs in South Africa to access finances comes due to
lack of education and training of managers. Massah and Wangai (2011) in their study
survey in Meru Central District, Kenya found that enhancement of business skills can lead
to lower financial obstacles and easier lending.
2.1.5 Location of SMEs
There have been various studies regarding location of SMEs and their impact on access to
finances. OECD (2008) in a study with Scottish SMEs found that businesses located in
urban areas face less restrictions in access to finance, compared to businesses located in
rural areas, because there was evidence that in rural areas, banks dealt only with certain
sectors (Deakins et al., 2008).
Business located in rural areas are not given same opportunities and conditions as the ones
in urban areas. Number of bank branches is much smaller in rural areas compared to urban
areas; therefore they can practice “monopoly power” by imposing high interest rates on
loans (Pandula, 2011). SMEs operating in rural areas face more obstacles also due to the
fact that their assets value is lower compared to those of SMEs operating in urban area,
less developed infrastructure etc. Fatkoi and Mazanai (2012, p. 58) in their study about
SMEs in South Africa elaborate disadvantages of SMEs in rural areas compared to ones
located in urban areas, especially due to the reason that banks in rural areas provide only
cash and are not authorized to grant loans. Tucker and Lean (2001, p.56) in their study
about companies in England and their access to finance found that there were no
differences in facilitations between companies located in rural and those in urban areas.
Kira and He (2012, p. 113) in their study, through regression analysis concluded that
location of companies is positively affects access to finance, therefore according to them
companies in urban areas have better possibilities for access to finances, compared to the
ones in rural areas.
2.1.6 Experience of manager
Entrepreneurs start their businesses for various reasons such as realizing a dream, disputes
with employer, unemployment or family heritage. Entrepreneurs with longer experience in
business have been found through different studies to be more favorable in terms of
11
lending from banks, compared to those with less experience in business. Experienced
managers prepare early reliable financial projections that result in easier access to bank
loans by being more convincing for bank officials. Xuegong and Xueyan (2011, p. 374)
found through regression analysis that differences in finding easier access to finances are
really small between managers with long experience and those with less experience. This
was also confirmed on a report from Stein- IFC and Grewe-USTR (October, 2011) where
it is explained how banks hesitate to lend money to women entrepreneurs, because of the
perception that women have lower education level and shorter experience in business.
Ahiakpor and Dasmani (n.d.) using the data from WB 2007 Enterprise Survey with 270
enterprises in Ghana, located mostly in the capital city of Accra, Kumasi, Tamale and
Takoradi found that years of experience of manager and his/her efficiency at work are
positively correlated only for up to a year, because later this correlation becomes
inefficient. Coleman (2000) in his study conclued that years of experience are positively
correlated to external finances. Abdesamed and Wahab (2012, p. 235) in their study with
76 SMEs in Tripoli, capital of Libya, also found a positive correlation between managers’
years of experience and their ability to borrow loans from banks.
2.1.7 Form of business organization
According to Agency for Registration of Businesses in Kosovo (KBRA, 2011), the most
common form of business organization is Individual Companies (90 %), followed by
Limited Liability Companies (5.8%) and Partnership Companies (3.2%). The status of
businesses is regulated by Law no. 2011/04-L-006 on Business Organization (Law on
amending and supplementing of the Law no. 02/L-123) approved by the Assembly of
Kosovo. Based on Article 48 of the Law for Business Organization, individual business as
companies run by the owner that has limited sources, receives all the profits and has
unlimited personal liability for all debts and other obligations regarding any losses caused
in the business. Such liabilities include all assets, i.e. properties that are directly or
indirectly owned by the person. Individual businesses are not considered a legal person.
Collective Associations (replaced definition of General Partnership) as per
abovementioned law are two or more persons /organizations cooperating in conducting
business activities based on written or verbal agreement. CA, same as Individual
Companies, have unlimited personal liability and are not considered either legal persons.
Since Limited Liability Companies are led by one, two or more owners, the profit is split
between them and all have shares of responsibilities based on their share of investments.
Contrary to ICs and CAs, LLCs are considered as legal persons. The status of LLCs is
regulated by Article 78 of the Law on Business Organization, which stipulates that the
owner is not liable about any debts or obligations of the company solely, just for being an
owner. Banks, having into consideration all form of business organization and their
features, are more reluctant in lending to individual companies rather than to limited
liability companies, mainly because of their insufficient assets that could serve as collateral
for the loan.
12
Mcintosh (eHow contributor, 2013) argued that limited liability companies can provide
more assets collateral for loans in case of default payment loan, compared to the sole
partnership. Harrison (2006, p. 2) argues that most of the businesses are registered limited
liability company (LLC), because the perception of the bank loan officer is stronger and
more reliable, making they lend easier to them. LLCs seem more reliable to the bankers
also because the number of founders is bigger and together they can pool more financial
resources. Businesses are registering as LLC also for safety reasons, to protect themselves
from personal responsibilities dealing with the debt of business (Harrison, 2006). Kira
(2012, p.115) argued in his study that sole proprietorship or partnership companies face
more obstacles in access to external finances compared to the limited liability companies,
because they are seen by lenders as companies with higher risk. Also Cassar (2004) and
Abor (2008) stated that the form of business organization does impact the access to
finances. Coleman and Cohn (2000) and Fatoki and Asah (2011), also find a positive
correlation between the type of business organization and access to finances. Harhoff and
Körting (1998, p. 1331)in their study, Lending relationship in Germany, concluded that
companies registered as LLCs find easier access to finances compared to companies with
other status, because they provide more assurance with regard to implementation of their
project through their sufficient collateral and loan guarantees, but they are also seen as less
risky businesses.
2.1.8 Value of assets
Value of assets is the amount of assets left as guarantee when SMEs are borrowing from
banks. Such assets include facilities, buildings, assets, houses, equipment, machinery,
vehicles, etc. Small and new enterprises face more obstacles when borrowing from banks,
compared to the medium enterprise, because in their first stage of operation, business starts
with smaller value of assets that makes them riskier for banks. Consequently, creditors or
bankers are more doubtful on their lending decision for younger or start-up business,
compared to the older business, mainly due to lower value of assets or total lack of assets.
Businesses located in rural areas are more constrained when it comes to borrowing,
because their assets are less valuable in the market, compared to those located in urban
areas. Phuong (2012, p. 103) in his study: What determines the access to credit by SMEs?
using World Bank Enterprise Survey (2009) concluded that the value of assets used as
collateral for loans, plays a crucial role for businesses that want to borrow loan from
creditors also in Vietnam.
Arpa, Giulini, Ittner and Pauer (n.d) in their study on the influence of macroeconomic
development on banks in Austria argued that the volatility of some asset prices compels
banks to be more strict during lending decision making, because of the risk that assets may
present in a long run. Therefore, this way the number of obstacles is growing for existing
business.
13
2.1.9 Business plan
Businesses should prepare a comprehensive and solid business plan, stating business
strategies, goals, marketing plan, and financial projections in order to convince banks or
creditors to finance their activities. Start-up businesses, due to lack of operation
background, have to convince bankers through business plans to raise funds for their
activity. Bank officials, apart from asking for financial statement from SME
representatives, they demand from them a business plan in order to find out how the SME
is planning to finance their operations. Mullen (Best Practice guidelines - SME Finance,
2012), in his study regarding challenges faced by SMEs on raising funds, called the
business plan the key of business that contributes in finding investors or banks to finance
their activities. Abdesamed and Wahab (2012, p. 235) in their study about 76 SMEs in
Tripoli, capital of Libya, found that business plans play an important role in assessment by
bankers for Start-up businesses looking for finances. Bank of England (1999) in their
report ‘‘Financing ethnic minority companies” concluded that lack of solid business plan
hindered ethnic minority businesses from accessing external finances. On the other hand,
Deakins, North, Baldock and Whittam (2008, p.5), in their study claimed that there is no
great importance in SMEs providing good business plans in overcoming restrictions
imposed by banks.
Nevertheless, managers or business directors of SMEs and start-up businesses should
prepare a promising and persuasive business plan and provide it to creditors in order to get
easier access to bank loans. They should present structure of the company operation, their
business goal, specific operation, financial projection and their plan haw do they will
realize their business activity and generate income. A hopeful or persuasive business plan
can help business to move forward, secure income, get easier access to bank loan and thus
they will be able to develop their business idea.
3
SMALL AND MEDIUM SIZED OF ENTERPRISE
This chapter outlines the characteristic of SMEs in Kosovo, the definition of SMEs, their
challenges and opportunities to growing in the market, contribution to economic
development, respectively creation of job opportunities and GDP growth, and financial
issues related to the growth of SMEs.
3.1
Definitions of SMEs
There are various definitions about Small and Medium sized enterprises
(UNCTAD/ITE/TEB/5). In order to define small and medium sized enterprises, different
countries set different criteria, based on which they define if a business is part of micro,
small or medium enterprises. However, most of the countries classify companies as micro,
14
small and medium enterprises based on these three components such as: number of
employees, value of assets or sales of the company.
The European Commission in 1996 adopted the definition for SMEs and later on, having
into consideration their role in economic development in the following years, on October
3rd, 2003 issued a new recommendation for SMEs which officially entered into force on
January 1st, 2005.
The category of micro, small and medium sized enterprises is made up of enterprises with
number of employees lower than 250 persons whose annual turnover does not exceed 50
million euro, and/or whose total annual balance sheet does not exceed 43 million euro.
(Extract of Article 2 of the Annex on Recommendation 2003/361/EC).
However, based on EU recommendation there are two main factors that indicate whether a
company is part of SMEs category. These two factors are the number of employees and
company’s turnover or balance sheet.
Table 1. Classification of Enterprise by Size in EU
Company
category
Medium sized
Small
Micro
Employees
<250
<50
<10
Turnover
Or
≤ € 50 m
≤ € 10 m
≤ €2m
Balance sheet
≤ € 43 m
≤ € 10 m
≤€2m
Source: European Commission, The New SME definition? Enterprise and Industry Publications (n.d.), The
New Thresholds, Art.2.
Enterprises should calculate their data based on these thresholds, than they will be able to
determine whether they are micro, small or medium sized enterprise. This new definition
of SMEs based on European Commissions policies aims at promoting entrepreneurial
investment, R & D, economic growth, improve access to capital and also takes better
account for possible types of relationships with other businesses.
World Bank classified companies into the group of micro, small and medium sized
enterprises based on the following thresholds: a max of 300 employees, $15 million in
annual revenue, and $15 million in assets (Bouri, Breij, Diop, Kempner, Klinger&
Stevenson, 2013). In United States of America, small enterprises are considered the ones
that employ fewer than 250 employees; medium sized enterprises are considered the ones
that employ fewer than 500 employees, while micro sized enterprise would be the ones
with less than 6 employees, whereas large enterprise are the ones employing fewer than
1,000 employees. However, according to the Small Business Administration, enterprises
are established in the basis of industry, therefore the definition of SMEs differs by country.
15
SMEs are known as the engine of a country’s economy due to their contribution in
employment, increase of people’s incomes, as well as contribution to overall economic
growth in developed or developing countries. Despite the important role that SMEs have
on economic development of a country, SMEs encounter different obstacles such difficult
access to loan, on their growth. Therefore, the European Commission as well as some other
countries have set the support for SMEs as one of their priorities in order to enhance job
creation, economic growth and social cohesion (Verheugen, 2005).
3.2
Small and medium sized of enterprise in Kosovo
After the war in 1999, a large number of enterprises comes to Kosovo with crucial role in
revival of economy as whole, by employing a considerable number of people, raising
incomes and overall living standard. Nowadays, couple of years after the recovery from
war in ’99, Kosovo is still is transition phase where entrepreneurship and creation of new
businesses is expected to play a crucial role towards free and modern economy (EC, 2011).
Size of small and medium sized enterprises in Kosovo is set by Law 03/L-031 that was
adopted by Kosovo’s Assembly in October 2008, as supplement to the Law2005/02-L5 on
support to SMEs. The only classification criterion that defines whether an enterprise is
micro, small or medium sized is the number of employees. This differs from European
Union countries where, besides the number of employees, they take into consideration also
the turnover or the balance sheet.
Table 2. Classification of Enterprise by Size and Employee in Kosovo (2012)
Classifications by size
Micro size
Small size
Medium size
Large size
Total
Number of employees
1
10
50
250
9
49
- 249
- or more
Number of
enterprise
109,800
1,508
224
58
111,590
Number of
employees
185,129
24,877
22,411
55,658
288,075
Source: 2012, Thematic Roundtable, No.4, Trade, Industry Customs, Taxation, Internal Market, Competition,
Consumer and Health Protection, Discussion Paper on the Area of Industry and SME, Table 3.
According to the data from Kosovo Business Registration Agency, at the end of 2012 the
number of registered micro, small, medium and large enterprises was around 111,590, with
288,075 employees, representing about 65% of total number of employed people in
Kosovo. Individual businesses dominate in the overall number of registered enterprises
with around 90%, followed by limited liability companies with 5.80 %, general partnership
with 3.20 % and the rest with 1%.
16
In terms of sectors in which SMEs are operating, most of them are focused on trade (50%),
followed by transport sector (14%), food products, beverages and tobacco (9%), hotels and
restaurants (9%)(KBRA 2011).Even though Kosovo’s market economy provides
facilitations for doing business such as low taxes, trade liberalization with EU countries,
low administrative costs for start-up businesses, stability of monetary system, which make
Kosovo an attractive destination for FDI compared to other countries, however, private
sector still remains an unstable backbone for the development of economy (Mauring,
2007).
There is a need to do more to attract foreign investors to invest in Kosovo in order to
develop private and public sector, employ more people, increase their incomes, and
develop the economy as a whole. Although Kosovo is known for its young population,
estimations from World Bank have shown an unemployment rate of around 48% mostly
amongst youngsters and women. Most of the SMEs are small companies dealing with
trade (50%) and a small number of them are larger and operate in manufacturing sector,
but the number of workers employed by them is really small. Consequently, businesses in
order to develop their operations, need to be financed through owns funds, credits or other
source of financing. Govori (2013) through her study has shown that the percentage of
loans given by banks is highest for the trade sector, followed by industrial sector including
production and construction sector. Banks must provide easier access and facilitations for
businesses dealing with manufacturing to raise the competitiveness, in order to impact
directly on reduction of imports and growth of exports.
Foreign Trade on December 2011 has shown a net deficit of € 2, 17 billion dominated by
imports. Main products exported by Kosovo are metal products (47%), ore products (30%)
and other products (food, machinery, appliance etc.) with the main destination markets in
Italy, Albania, Germany and Republic of Macedonia. World Bank in its report, Doing
Business 2013, ranked Kosovo 126thout of 185 economies as the easiest place to start a
new business. Kosovo Government has adopted the, SME development strategy for
Kosovo 2012-2016, with vision to 2020 with the main activities directed towards
development of SMEs, where the second most important goal is enhancement of access to
finance.
3.3
Contribution of SMEs to economic development (GDP) of Kosovo
The SMEs are accepted by most policymakers as the engine of an economy in transition,
both in developing and developed countries. Besides the positive impact that SMEs have
on reducing unemployment, raising incomes of people and overall development of
economy, their contribution is considerable also on GDP growth. The Organization for
Economic Cooperation and Development (OECD) reports that more than 95% of
enterprises in OECD area are SMEs that are expected to play a crucial role in growth of
GDP and that will employee the largest part of workers.
17
In the table below we can see the contribution of micro, small, medium and large
enterprises in Kosovo’s GDP according to Tax Administration of Kosovo (TAK):
Table 3. Participation of Micro, SMEs Annual Turnover in GDP of Kosovo
Size of enterprise
Micro
Small
Medium
Large
Total
Number of
enterprise
Turnover (‘000€)
14,968
1,210
185
58
16,421
656,885
667,585
369,455
528,558
2,222,485
Share of GDP (%)
16.79
17.07
9.44
13.51
56.81
Source:2011, MTI, KBRA, SMEs Development Strategy, 2012-2016, SMEs Annual Report 2011, Table 6.
Based on the data above we can see that total annual turnover of SMEs in Kosovo’s in
2010 was 43.30 %. As we see, the percentage of SMEs’ participation in the GDP is quite
high, giving them a significant role in the economic development, despite all the obstacles
faced by them in their operation. Such obstacles include access to finance, business
environment, unfair competition, not loyal economy, etc. Therefore facilitating and
creating better business environment for SMEs will have a significant effect on economic
development and in growth of the percentage of participation in Kosovo’s GDP.
3.4
Factors influencing SMEs growth
SMEs are known as the backbone of the private sector, playing a crucial role in economic
growth, development and prosperity of the country. Their growth is really important,
because it contributes to increased employment of population, increased income for people
as well as overall developing country’s economy. Growth of SMEs depends on a number
of external and internal factors such as: access to finance, competitiveness, infrastructure,
rule of law, government policies, level of corruption, etc. Unreliable supply with electricity
and water, as well as access to finances and insufficient rule of law are some of the main
barriers that impact the growth of SMEs in Kosovo (EC Kosovo Progress Report,
2010).Based on the impact of regulatory and legislative frameworks in growth of SMEs,
Ministry of Trade and Industry together with the Government of Kosovo have stated that
more effort should be done in order to get rid of such barriers.
Many researchers have emphasized in their researches different factors that impact the
growth of SMEs. Aidis (2003, p.20) classified the factors that impact the growth of SMEs
in countries in transition as formal (tax and business legislation) and informal (corruption
and not successful rule of law). According to World Bank (2010), obstacles faced by SMEs
18
in their growth and further development have remained the same in Kosovo since post-war
period. These obstacles include unfair competition, public services and informal economy.
Moreover, inefficient juridical system, large presence of corruption in the institution and
government of Kosovo are making difficulties for the development of SMEs operation.
Day after days the challenges of SMEs to grow up in the market are becoming increasingly
more difficult. Existence of these problems keeps hindering the development of activities
by SMEs and they keep constraining them from finding sources of finance in banks.
Morrison (2006) in his study claimed that some of the factors such as political, social,
technological, environmental, economic and legal that impact the growth of enterprises are
not controllable. SME operations are largely affected by these factors, although
management of businesses cannot control them, therefore enterprises must adapt to the
environment in order to survive in the existing market. Costumer and market, recourse and
finance are the main factor which have a crucial role on ensuring the success of SMEs in
Thailand (Chittithaworn, Islam, Keawchana and Yusuf, 2011).
The main barriers to development and growth of SMEs in Kosovo, according to Riinvest
(2013), are institutions driven by unfair competition, costs and access to finances,
corruption, crimes and political instability. Law enforcement, corruption, fiscal evasion,
informal economy also play significant role on development of small enterprises. Ayagari,
Demirguc-Kunt and Maksimovic (2006) stated in their study that all above mentioned
factors do not have the same impact. In their regression analysis, they found out that
political instability and crimes affect directly the growth of a company. Another obstacle
for SMEs common in transition countries is access to finance. Banks, due to the risk posed
by factors such as inefficient judicial system, hesitate to lend money to businesses
becauses, in case of existing Non performing loans (NPL) of businesses they will have to
confront with them.
According to Bertelsmann Stiftung’s Transformation Index (hereinafter: BTI), 2012, even
though there has been quite a lot done in recovering Kosovo’s economy, there are still
challenges that have to be addressed such as inefficient administrative structures,
corruption, politicization of public administration, informal economy, etc. Corruption is
quite widespread in our country and according to European Commission (Kosovo, 2011
progress report) corruption continues to remain a severe problem in many areas in Kosovo.
This phenomenon is harmful and destructive for development of Kosovo’s economy,
especially the private sector because it hinders and stops many business activities, making
their development really difficult. Thus creating an adequate legal framework will have a
major impact on the decreasing of obstacles of SMEs to develop their activity. However,
although there are different obstacles that SMEs face on their development, still there is no
method to measure which are the adequate factors that impact the growth of SMEs.
Except the external factor which already have been mentioned above, there are also some
internal factor which can really have impact on the development of SMEs. Some of those
factors can be firms characteristic such as management of firm, organizational structure of
19
the company, shareholders, employee, firm performance, innovation, technology,
marketing etc. In order to develop their operation activity SMEs need to be financed.
Access to finance of SMEs except external factor it is dependent also on the internal factor
such as managerial ability, employee’s ability and overall firm ability. Thus, by preparing
persuasive business plan, accurate financial statement they will find easier access to
finance.
3.5
Financial issue associated with SMEs
Most of the micro and small businesses operate in retail sector, although a large number of
them have stagnated as a result of low productivity. Sources of finance are crucial for the
growth of micro, small and medium sized enterprises, enabling to continue their operation
or invest in something new and increase their competitiveness, use advanced technology to
accelerate their operations and grow further their business. Usually, micro, small and
medium enterprises finance their operations from their internal funds or they borrow from
family or friends before going to banks to ask for loan, because of the high interest rates
and other restrictions imposed by banks.
Micro and small sized enterprises are considered to be more constrained in terms of the
ability to get access to finance, compared to larger businesses due to existing asymmetric
information, high demand of collateral, valuable assets etc. Due to the lack of financial
sources, many SMEs have quit their investment and a number of businesses are vanishing.
Most entrepreneurs start their business based on an existing business idea and they do not
thing further on inventing something new. Consequently, we have SMEs with low
productivity and their contribution to the growth of economy is really low. In order to
survive in the market, they need to generate revenues to further expand and grow their
businesses. Unfortunately, the number of internal and external sources of finances for
SMEs in Kosovo is limited. Currently SMEs use loans as the only external source of
finances, because the number of other sources such as foreign investments and grants has
decreased in the recent years (CBK, 2013).
The role of bank credits is significantly important having into consideration that it remains
the main external source of financing for SMEs, because other alternatives for financial
resources through the capital market are extremely low. Banks impose a number of
constrains for credit borrowers for their own safety reasons and to decreases the credit
exposure, because they are afraid that borrowers may not pay the instalments and they may
not be able to collect NPL (bad loans), having into consideration the inefficient judicial
system in our country. This is one of the main challenges encountered by SMEs in their
development, which also pushes bankers to tighten credit procedures.
World Bank (2012), due to problems with judicial system, implementation of laws and
regulations has ranked Kosovo at the end of the list for many years. Having an inefficient
juridical system makes banks institution to feel really endangered in their lending activity
20
because they will find really difficulty to collect the collateral left by borrower for loan
security.
Also informal economy recent year is more prevalent in Kosovo; which is quite harmful
for the development of economy and creation of non-riskier market. Banks on the other
hand, being the only provider of financial resources for businesses, are misusing this
advantage and keeping high interest rates and further overloading business. Although the
number of banks entering into market has increased throughout years, the interest rates on
credits are still the highest compared to the countries in the region. The high interest rate
charged on loan is too expensive for business operating in the market of Kosovo and often
even unaffordable for them.
Based on a survey carried out by Riinvest Institute named: Banking sector Facilitators or
barriers(2012), banks in Kosovo have potential to increase the credit for SMEs due to the
liquidity reserves held by them, but unpredictability of companies’ performance is
increasing the risk and conservatism of banks. Based on interviews with representatives of
eight banks in Kosovo, with regard to why banks are keeping such high interest rates on
loans and why they are restricting SMEs from borrowing, the answer was due to high risk,
business environment, lack of adequate projects, inefficient judicial system, major delays
on resolving issues and collateral execution, improper financial statements, inadequate
business plan, operational costs, lack of registration of apartments, etc. Lack of competitive
financial institutions in the market and the business environment are main factors that
make banks maintain the highest interest rates on credit in the region.
Therefore, an improvement of business environment would also contribute to lowering
operational cost of the banking sector as well as lowering interest rate for loans, and at the
same time increase opportunities for borrowing cheaper bank loans. Part of responsibilities
falls on companies, because they should prepare comprehensive financial statements and
adequate business plans in order to be more reliable and visionaries towards the
development of their business activity and thus they will have easier access to bank credits.
The high level of collateral required by banks for obtaining loans is also a major obstacle
that is sometimes for SMEs to overcome.
All these bank restrictions for loan borrowers are often discouraging many entrepreneurs,
some of whom do not even try to get a loan. Such facts also result in failure of existing
businesses or the ones in early stage of development, who may have had potentially good
idea to open new business and create new opportunities.
The outcomes of the survey regarding the financial obstacles faced by SMEs in Kosovo
(BSCK report, 2012), have shown that there is a potential number of entrepreneurs that
may have a potentially good idea for development, but are discouraged to apply for loan
because as they feel that they will be rejected (Kon& Storey, 2003).
21
Figure 1. Discouraged Borrower
Total population
(500 SMEs)
Loan
Application
Did not apply
Did not have
need loan (163)
(326)
Discouraged borrower
Applied for loan
(163)
(151)




Successful applicants
Rejected
(139)
(12)
Source: 2011, BSCK Research Report, and
own calculation based on the number of SMEs sample, Figure 4.1.
22
High interest rate
Not confident
about success of
application
Not able to fulfil
bank requirement
High collateral
requriement
Such SMEs have mentioned different reason and obstacles that they are facing in access in
a bank loan such as high interest rates, difficult procedures, large number of document,
collateral requirement, land registration on business owner’s name, etc. Thus, SMEs taking
in consider all these obstacles that event try to apply for bank loan because they know that
will know that will not be able to fulfill all those criteria.
Based on the conducted survey we had divided on the figure 1 the borrower according to
their decision.
Moreover, on the figure above can be seen the number of SMEs that applied for a loan,
number of SMEs that didn’t apply, number of SMEs that did not need a loan and group of
discouraged borrowers. The figures above shows that due to the restrictions imposed by
banks, a large number of businesses or SMEs hesitate to borrow and do not even try to get
a bank loan. The figures shows that from the conducted survey with the SMEs 151
enterprise applied for loan, where mostly of them 139 has borrowed loan from bank while
the other 12 has been rejected due to different reason.
However, from the other enterprises 326 who didn’t applied for loan half of their number
didn’t have need while the other half hesitate to ask for loan due to the restriction and
difficult condition opposed by banks. High interest rates, sum of collateral requirement,
term of loan and doubt that their requirement will be approved by banks, keep a number of
businesses away from developing their operation activities. Banks, due to unreliable
project applications submitted by businesses and risky market have tightened their lending
activity. Thus, if SMEs want to borrow loans from banks they must meet and fulfill all
bank requirement and loan terms, but on the other hand source of finances play a critical
role in development of small and medium sized enterprises and are really important
especially in countries in transition country where private sector plays a crucial role in
recovery of the economy.
4
BANKS AND FINANCIAL INSTITUTION OF KOSOVO
4.1
History of banks’ development in Kosovo
Kosovo banking system started from the scratch after the war in 1999. Since early 50s,
banking sector was part of Former Yugoslavian banking sector, composed by National
Bank and several commercial banks owned and governed under Yugoslavian autonomous
system (Bonin & Wachtel, 2004). After the fall of Former Yugoslavia, the banking system
began to be destroyed.
In Kosovo, just after the war the creation of the banking financial sector started. In the
summer of the same year, there was already established Banking and Payment Authority of
Kosovo (BPK). During that time, all payment functions were undertaken through BPK and
23
later on December 1999, EBRD jointly with IFC and IPC (International Project Consult)
established the Micro Enterprise Bank which in 2003 was transformed to Pro credit Bank.
In 2001 there were more banks established in Kosovo, amongst them New Bank of
Kosovo, Commercial Bank, Kasabanka, Bank for Business, Kosovo American Bank,
Prishtina Crediting Bank and Credit Bank which in 2006 went bankrupt. Albanian
Commercial Bank (BKT) was established in 2008, as well as Commercial Bank of Serbia,
Turkish Economic Bank (hereinafter: TEB) and NLB (from joining of the Kasabank and
New Bank of Kosovo). In the beginning of 2013 the Turkie Is Bankasi A.S. got licensed
from the Central Bank of Kosovo, but it has not started its operations yet.
Table 4. Number of Financial Institution Operating in Kosovo
Name of financial
institutions
Commercials bank
Insurance company
Pension funds
Financial
auxiliaries
Microfinance
institutions
June 2009
June 2010
June 2011
June 2012
8
11
2
28
8
11
2
29
8
11
2
32
8
13
2
36
19
17
17
19
Source: 2012, CBK,Financial Stability Report, Table 4.
As we see from the table above, the structure of financial institutions has remained almost
the same, with the same number of commercial banks and pension funds, followed by a
slight increase in the number of insurance companies and microfinance institutions, while
the number of financial auxiliaries has increased every year. The number of commercial
bank remained the same for some time until last year, when Turkiye Is Bankasi entered in
market, bringing the number of commercial banks to 9.
The main activities of banking sector in Kosovo are focused on crediting and depositing.
According to the Ministry of Foreign Affairs of Kosovo (Kosovo economy, 2010) banking
sector, despite the recent global financial crises, has remained stable, liquid and solvent
and continues to operate with the best performance compared to other sectors of economy.
Beside the increasing number of insurance companies in Kosovo, their activities in
expanding and offering new products for consumers has not increased yet. In 2002, the
Insurance Association of Kosovo (hereinafter: IAS) was established with the main activity
of improving, assisting and providing training for the staff of the insurance industry in
Kosovo (IAS, 2013). Pension funds have remained the same in terms of numbers and are
regulated by Law No. 04/L-101 approved by the Assembly of Republic of Kosovo. The
number of financial auxiliaries has increased year by year, expanding the financial support
24
for the market economy. Other financial institutions such as Microfinance Institutions have
also increased in number and they have played an essential role in the growth of economy
by granting loans to poor businesses and entrepreneurs. This has also been followed with
an increase in number of MFI providing support in the lending activity for businesses.
These institutions provide starting capital for starter businesses and enable them to develop
their ideas. Credit from these microfinance institutions is usually characterized by higher
interest rates compared to credits from banks, because the loan consumers are riskier
compared to the bank loan consumers.
4.2
General characteristic of banking system in Kosovo
The Assembly of the Republic of Kosovo based on Article 65 (1) of the Constitution of the
Republic of Kosovo has approved the law on banks, microfinance institutions and nonbank financial institution in April 2012. The purpose of this low is to foster and maintain
stable financial system through promoting the sound and prudent management of banks,
microfinance institution and other non-bank financial institution and providing an
appropriate level of protection for depositor’s interests (Constitution of Republic of
Kosovo).
Banking system is getting more credible and sustainable despite the difficulties that
Kosovo’s economy is facing every year, however it is characterized with the highest
interest rates on loans in the region. Representatives of eight banks in Kosovo, in a
research report carried out by Riinvest Institute, explained the reason how the country
continues to have sustainable and healthy financial stability. They claimed that advanced
criteria and procedures applied by banks on assessing loan applications, CBK role on
preserving the implementation of credit origins, absence of Greece banks in Kosovo and
lack of trade between Kosovo and Greece has created advantage for Kosovo in reducing
exposure to the risk (Riinvest, 2012).
Among the financial sector assets in Kosovo, banks dominate with a total share of 74.6%
in June 2012, followed by pension funds 18.6%, microfinance institutions 3.4%, insurance
companies 3.4% and other financial institutions (CBK, 2012). Banking sector comprised
nine commercial banks, seven of which are owned by foreign entities and two are
domestic. Foreign banks manage around 90.0% of total value of assets, while the
remainder is managed by domestic banks. Their presence in Kosovo has contributed a lot
in providing advanced and accurate services for clients. There are in total 310 branches and
sub branches, 415 ATMs, 8,361 pos, and 55,292 e-banking accounts (Bank Association of
Kosovo, 2012). They offer a wide range of bank and financial services. Their activity is
dominated by loans with a maturity around 15 years, where 69.9% are entrepreneur loans,
52.7% trade loans, 24.8% industry loans, 3.5% agro loans and 30.01% household loans
(Kosovo Banking Association, 2012). The portfolio of all bank loans is 1.77 billion euro,
around two-third of which is lent to businesses, mainly to enterprises (CBK, 2012). The
structure of bank loans is Kosovo are classified into loans for trade, manufacturing,
25
construction, agriculture, mining and other services, while in terms of maturity they are
classified into loans with maturity term up to one year, one year to two years and over two
years. The credits of over 10,000 euros are guaranteed with collateral or guarantors (Bank
Association of Kosovo 2012). The required collateral for a guarantee for banks is
unaffordable for many SMEs. If we look into the survey of SMEs, conducted by Business
Support Centre of Kosovo (hereinafter: BSCK) the ratio between the loan and collateral
requirement is 250 % (BSCK Report, 2011).
The operation of banking sector in Kosovo is regulated by the rules set by CBK. The
banking sector is dominated by foreign banks managing around 90.00% of total bank
assets, while the domestic bank manage the remaining 10%. Banking sector is considered
to be among the safest sectors, sustainable and most productive, reaching a total value of
2.65 billion euro or 54% of GDP.
Financial activity of banking sector in Kosovo are primarily concentrated on loans ,
consisting 67% of total bank assets, followed by cash and balance with CBK 11.3%,
balance with commercial banks 10.00%, securities 8.1% etc. Based on the rules set by
CBK, banks must not lend more than 80% of their client deposits (Whittle, 2012).
According to survey conducted by Riinvest Institute (2012), the banking sector has
increased its net profit until 2008 (34 mil euro), followed by a sharp decline in 2009 on its
net profit, which may have come as result of global financial crises, to increase again in the
following years, reaching 37 million euro in 2011. Their survey results has shown that
based on banking sector indicators such as ROA (1.4%), ROE (14.5%) and NPL (6%),
Kosovo banks are the safest, most sustainable and profitable compared to the countries in
the region.
Figure 2. Profitability Indicators of Eastern Europe Banking Sector
Source: 2012, WB statistics, Country Report of Kosovo, Macedonia, Montenegro, Albania, Bosnia and
Herzegovina and Serbia and own calculation.
26
In Figure 2, we will compare the ROA, ROE and NPL ratio of banking sector in Eastern
Europe countries.
In their annual report (2012), CBK reported a decline in the main profitability indicators
during the year before, due to lower profit of banking sector. The ROA ratio shows the
company’s profitability and gives an idea on how the management is generating revenues
by utilizing assets or employees. As we see from the figure above, the percentage of banks
ROA has experienced short decline during the last years from 1.4% (2011) to 0.7% (2012).
Although it remains at 0.7 %, it has performed best compared to the Eastern Europe
countries, except for Bosnia and Herzegovina which stood at the best point of 0.9%.
The ROE ratio is useful in measuring the profitability by comparing it with other
companies in the same sector. Despite the decreasing percentage of ROE ratio in Kosovo
during the last years, dropping from 14.5% (2011) to 8.1% (2012), it is still the highest rate
of return compared to Eastern Europe countries and it is around 4 times higher than in
Serbia (2.1%), and 4.4 times higher than in Montenegro (-18.3%), although this loss was
caused by two largest bank as result of decline in extraordinary revenue from 71 mil euro
to 33.6 mil euro (CBM, 2012, p. 56). This ratio leads to the perception that banking sector
in Kosovo is operating with higher profitability than banking sectors in regional countries.
Also the nonperforming loans (NPL) stand on the best point compared to the countries in
the region. The percentage level of nonperforming loans shows the quality of loan portfolio
of banking sector per country. The ratio of NPL marked an increase compared to the
previous year (5.7% in 2011 compared to 6.4 % in 2012) representing the highest level of
NPL since the establishing of banking system in Kosovo (CBK, 2012). However, the level
of NPL in Kosovo (6.4%) is the lowest in the region, followed by Macedonia (9.7%),
Bosnia and Herzegovina (12.6%), Montenegro (17.6%), Serbia (18.6%) and Albania
(22.5%). This ratio makes banking system in Kosovo safer compared to the ones in other
countries of Eastern Europe.
Banking sector is also characterized with a high rate of concentration; however the
concentration rate has been characterized with a declining trend in the present year due to
constant growth of smaller bank activities (CBK, 2012).
Although the number of new banks entering in the market is increasing, there are still high
interest rates charging the loans, which are the highest in the region. Banks are keeping
high interest rate on loan due to the instability and market risk. However this interest rate
charged on loan are making very difficult operation of business in the economy and such
are increasing the number of NPL due to the expensive price which cost for borrower.
Market imperfection, informal economy, high interest rate of bank loans, corruption and
ineffective juridical system are some of the obstacles that are hindering the development of
SMEs.
27
4.3
Banks role in front of SMEs
The role of banking sector activities is vital to enterprises. They provide people and
businesses a great range of product and facilitations for doing business. SMEs through
banks can execute many business transactions, deposit money and in case of insufficient
means for their businesses activities they can borrow financial sources from banks.
Banking institutions serve as financial intermediaries by collecting money from those that
make deposits and crediting or lending it to those that need it. For micro and small
businesses, banks provide opportunities for obtaining financial resources and help them
develop their business activities. Banks perform many helpful activities for SMEs
including maintenance of a stable financial system, giving money and credits to businesses
in need, help in performing, developing as well for further expanding their businesses.
They provide a kind of loan with different terms, always adapting to the business
requirement and needs.
In developed countries as well as in developing countries, banks are known as the only
source of external financing for SMEs. Banks serve in Kosovo as the only source of
internal source of finance for SMEs operating in the market. They provide a variety of
bank credits which can be very useful and crucial for business operations. SMEs in need
for financial resources to finance their business activities, they can use bank loans, but in
order to obtain them they must apply and fulfill all the criteria set by banks.
However, lending conditions for micro and small businesses are often difficult or
unaffordable for them. Most of the start-up businesses or small businesses, in the early
stage of their operations, are characterized with lots of obligations towards suppliers,
insufficient sources of financing to meet their business obligations, unfair market
competition and other major obstacles imposed by informal market economy. In order for
them to survive in the market, they need to be further financed, although the high cost of
loans and high interest rates applied by banks are not in best favor for business.
Thus, a number of micro and small enterprises find it really difficult to obtain a bank loan
and many of them do not even try, giving up their business development idea. As result
there exist e potential number of small enterprise which need loan but due to the terms and
unaffordable conditions imposed by banks doesn’t even try to ask for loan. On the other
hand, large businesses find easier access to get financial sources due to their operation
background in the market, available business information and valuable assets. These
businesses can develop their business operations easier in the market and will have higher
probability of getting financial supporters. Moreover, operation of SMEs as well as the
development of them is strongly affected by the banks decision on lending. By being a
sustainable and effective banking sector has given a large contribute on the development of
the financial system in Kosovo.
28
Banks provide more facilities for business which have good history of operation in the
market compare to the start-up business or new one due to the existing of asymmetry
information. Thus start-up business or young business should provide reliable information
regarding their business in order to find easier access to bank loan. As we see, banks’ role
among to SMEs depends also on the business’ role among to bank. However they should
cooperate with each other in order to be also those facilitated from banks. Banks’ role
among SMEs is really important and significant on enhancement and development of their
operation activities.
4.4
Bank’s competition and concentration in Kosovo
Banks are known as one of the most important financial institutions for the development of
economy in Kosovo. Through their activities, banks help people, entrepreneurs and
businesses to survive the challenges imposed by market economy. Banking sector provides
businesses in Kosovo a modern and efficient payment system and wide range of financial
services (Bank Association of Kosovo, 2012). They facilitate them by lending to those in
need to develop their business operations, from the deposits collected from other people.
Thus banks have a significant role on the development of the SMEs.
Banks in Kosovo are characterized by a traditional banking system, with majority of assets
credited to enterprises (Bibolli, 2012). According to CBK, every year there is a slight
growth in number of credits given to businesses, and these have driven further the
development of enterprises and market economy as a whole. The total number of loans
issued by banks in June 2012 reached an amount of 1.77 billion euro or 36.2% of GDP
(CBK, 2012).
In order to measure bank’s competitiveness there are various variable used including
competitive environment, bank concentration or ownership structure (WB, 2003).There are
totally eight banks in Kosovo, with majority of them owned by foreign entities and just two
of them being domestic ones. In 2013 the number of banks has increased with a new one
entering the market, new Turkish bank, bringing the number of banks to nine.
Overall, the banking system infrastructure continues to remain almost the same with the
same number of branches and sub-branches (310), 415ATMs, 8361POS and 55292ebanking accounts (Bank Association of Kosovo, 2012). Although the number of banks
entering the market has increased over years, banking market is characterized with high
rate of concentration. Based on measuring by CBK through Herfindahl Herischman Index,
banking concentration rate has slightly decreased with small banks entering Kosovo
market.
Although the concentration rate has decreased in recent years, banking system in Kosovo
continues to be characterized with high rate of concentration focusing mainly on three
29
largest banks that managed 74% of total assets, 74% of deposits and 71.7% of total loans at
the end of 2011 (Riinvest, 2012).
Kosovo Competition Authority (2012) argued on their report that bank competition is
depending strongly on financial market stability. Although Kosovo market economy is
characterized with sustainable financial system, concentration of banking sector continues
to remain high despite the decline in concentration level from 2,101 points in 2011 to
1,972 points in 2012 (CBK, Financial Stability Report, 2012).
4.5
Micro finance institutions (MFI)
Target markets of MFIs are small and new businesses, small individual and household
businesses, including hairdressers, tailors, small cosmetic shops, small farms, handicraft or
craftsmanship, etc. (Ledgerwood, 1999). Such credits help start-up businesses grow and
develop their activities. According to Wasmus (KEP,2012) banks in emerging economies
lend more to large corporations leaving SMEs aside, whereas microfinance institutions are
oriented in lending to micro entrepreneurs or start-up businesses.
Having into consideration that small enterprises have a significant role in economic
development by hiring people and generating incomes, support to this sector is really
important. These credits are small loan amounts with short maturity time of up to three
years, but usually have high interest rates. Some of these loans are also dedicated to
people living in rural areas to facilitate their business operations and improve their living
conditions. Certain MFIs provide people with mortgage loans.
Moreover, the number of MFIs operating in Kosovo is relatively small, around 14. Most of
them are part of the Association of Microfinance Institutions of Kosovo (AMIK). AMIK
has been established in 2012 by MFIs to support and finance micro businesses, start-up
business, people with low incomes etc.
MFI system and its functioning in Kosovo is regulated by Law No.04/L-093 on Banks,
micro finance institutions and non-bank financial institutions. Also by the end of August
29th of 2013 pursuant to Article 35, paragraph 1.1 of the Law No. 03/L-209 and Article 102
and 114 of the Law No. 04/L-093 the Board of CBK has approved the Regulation on
reporting of microfinance institution to the CBK (CBK, 2013).
Due to the bad economic situation in Kosovo, most MFIs operate with the status of an
NGO, even though during last year, with the adoption of Law by Assembly of Kosovo on
April 12th, 2012 it was possible to transform NGOs to individual businesses or Joint Stock
Companies.
Micro finance institutions are financial institutions whose main activity is lending to small
enterprises in need and family businesses with low incomes. Loans are dedicated mainly to
30
poor businesses and people, especially those that have major constrains in access to bank
loans such as start-up businesses.
Nowadays, role of these micro finance institutions is becoming very important as they
provide a wide range of loan products for businesses having difficulties with their finances.
These loan helps business with low income to develop their activity, however the interest
rate charged on loan are high.
Finca Kosovo (MFI, 2013) except the loan which are mostly common also on other micro
finance institutions has introduced a new loan product for individuals and entrepreneurs in
its range of products to facilitate them with financing. The product is called group loan
were people with low level of incomes can apply together, and are not obliged to leave
anything as collateral, but they only have to guarantee for each other and also pay higher
interest rate for the loan. Thus, if entrepreneurs are looking for a loan or credit to develop
their businesses, having no possibilities to get it from banks or any other financial
institution, they can try to find one or more individuals to get a group loan from the micro
finance institutions. Each co-creditor can use their part of the loan for business or personal
purposes and has to pay back for it.
This is an example that indicates that micro finance institutions are continuously trying to
find the easiest way for sources of finance. As we see, these institutions are providing
better alternatives in supporting poor businesses and entrepreneurs living mostly in rural
areas, to develop their businesses which are mostly unsupported by banks. This will help
improving their business operations as well as living standard. MFIs in Kosovo provide
different loans depending on the purpose of loan. Such loans include business loans, home
improvement loans, individual loans, group loans, agro loans, etc. Agro loans are types of
loans dedicated to farmers living in rural areas dealing with agriculture activities such as:
planting, growing and rising of livestock etc. Those people need to be financed or find
source of finance in order to keep and develop their business running. Rural entrepreneurs,
due to low financial source and priceless collateral, find it really difficult to borrow loans
from banks. In order for them to develop their activity, their only option remain agro loan
from MFIs, who provide favorable conditions for borrowers and entrepreneurs.
Nowadays, the competition on this sector has begun to grow as well they are expanding
their wide range of product in order to meet the customer needs. This is demonstrated also
with the agro loan mostly dedicated to the peoples living in the rural areas. Providing agro
loan to those people have a significant importance on the development of those areas as
well as their living standards. This has a significant role on the development of the agro
sector due to their restriction opposed by bank on crediting. Agro entrepreneurs which find
access to the MFI loans will find it easier to develop their farming as well create a job
opportunity, hire people living there and grow up their business. Considering the agro
sector which employs the highest percentage of workers in the SMEs, providing facilities
of doing business to this sector will contribute except on the decrease of import also on the
31
grow up the number of employees. Thus the MFIs role day by days is getting really
important on providing loan to the poor business and people living with low monthly
income.
Despite the activity of lending to small business, there are also a small number of money
transfer agencies, whose main activity is money transfers in and outside the country,
execution of payments etc. These agencies help businesses a lot by providing quick money
transferred from abroad as well as in making their business or individual payment. It is
rather safe to work with these agencies, whilst money is transferred quickly and accurately.
A number of Non Bank financial institutions also operate in Kosovo that are regulated by
the Law No. 04/L-093 for Banks, micro finance institution and non-bank financial
institutions. The function of these agencies is to provide people with business loans,
mortgage loans or facilitate their operations through financial or operative leasing.
5
METHODOLOGY APPLIED
The following chapter represents the most important part of this research as far as the
purpose of this topic concerns. Initially it will provide description of data collection
process, description of sample data and description of questions used in the survey for each
variable, to continue further with interpretation of the result, and the final part will provide
the conclusion.
5.1
Description of variables
This study is based on the collection of primary and secondary data. The primary data is
collected through questionnaires and interviews conducted with representatives of SMEs
for the purpose of this research, while the secondary data will be based on literature review
of other authors described in the second chapter. The data used in the model are obtained
from the Business Support Centre of Kosovo, whose main activity is facilitating
entrepreneurs and start-up of business by providing training and consultancy in different
areas such as: how to write plan business, entrepreneurship, marketing and market
research, finance etc. The research report was compiled during three consecutive years
starting from 2010, using the same samples of SMEs. It intentionally highlights the main
barrier for the development of businesses in different regions of Kosovo.
The data used in this survey are from 2012. The survey consists of nine chapters, including
all relevant questions related to SMEs, such as fact sheets, business activity,
obstacles/barriers to business growth, development trends, innovations, taxation,
computerization of business and staff. The random samples include 488 SMEs from a total
population of 44,303 SMEs, and the survey was implemented in seven regions of Kosovo:
Prishtina, Peja, Mitrovica, Ferizaj, Gjakova, Gjilani and Prizren. 34 researchers were
32
engaged to survey mostly the executive management of SMEs. The targeted small and
medium enterprises were of different sectors such as those dealing with construction, trade,
production, agriculture etc. Most of them operate on the retail sector. Therefore, in order
to construct the Logit empirical model for the topic some of the variables from the question
of different chapters used in the questionnaires have been selected. Below I will describe
each variable taken from the survey and used for the construction of Logit regression
model whose characteristics may mostly influence SMEs to access bank loans. The
variables are separated in two groups: dependent variable (loan) and independent variable
(gender, age of firm, size of business, form of business organization, location of firm,
business plan, education of manager, experience of manager and the value of assets).
The table below summary a maximum number of observations 486 and provide
information regarding the mean, median, standard deviation, minimum number and
maximum number that have reveal from the observation used.
Table 5. Descriptive Statistics of Variable Used in the Logit Regression Model
Variable
Obs
Mean
Median
Std. Dev.
Min
Max
Loan
456
0.298245
0
0.45799
0
1
Gender
481
0.153846
0
0.361176
0
1
Location
482
0.881742
1
0.323248
0
1
Age firm
465
9.251613
9
5.448339
1
26
Form
of
business
organization
480
0.897916
1
0.303073
0
1
Experience
of manager
464
1.655172
1
0.800684
1
3
Business
plan
478
0.416318
0
0.493464
0
1
Education of
manager
447
0.362416
0
0.481236
0
1
Size firm
459
8.840959
3
20.7788
0
157
Value assets
486
397108.4
13000
3961804
0
81,000,000
Summarize of loan, gender, location, age of firm, form of business experience of manager,
business plan, and education of manager, size of firm and value of assets:
33
Loan- is a dependent variable of the Logit regression model. The question was provided in
the fifth chapter where the respondents should have answered the question number 6. Have
you borrowed a loan from a bank? The respondents had to choose between three answers:
1. Yes, 2. No. I haven’t tried? And 3. No. I’ve tried, but I’ve been refused. The number of
respondents that selected answer number 1 was 136; the number of respondents that
selected the answer 2 was 308, and the other respondents that selected the third answer was
12, whilst 31 did not respond to this question. Half of the number of respondent which has
choose the answer number 2 was as a result of difficult condition on crediting from banks.
Gender – This is a dummy independent variable of the Logit regression model. Gender
was one of the primary questions on the survey related to the executive managers of SMEs,
where they have to indicate their gender. Gender: 1. Female 2. Male. Most of the
respondents were male (407) while 74 were women respondents. On different studies this
variable seems to be important on the decision of finding easier access to bank loan.
Location- This is also a dummy independent variable. Depending on the location, the
question was referred to the place where the business operates and the respondents had to
choose between two options 1.Urban and 2. Rural area. This question was part of the
second chapter of the survey; in the fact sheet 2.The enterprise operates in 1. Urban 2.
Rural. The percentage of SMEs operating in urban area was considerably higher 87.27%
compared to SMEs operating in rural area 11.7% and the other part that did not respond
1.03%.
Age of company- This is also an independent variable. This variable presents the total
number of years the company has been operating in the market. The question is in the
second chapter 4.The year of establishment: Write the year when your enterprise began its
operation? The research was conducted at the end of 2011, so the enterprises established in
2012 have been removed from the survey. There was also a number of companies that did
not respond to this question. This variable seems to be very important on the decision of
banks on lending to the SMEs. Being older enterprise with many years of operation in the
market seems to have greater facilities in front of bankers compare to the new business or
start up business which operates with few year in market.
Size of company -This is an independent variable of the Logit regression model. The size
of company is measured by its number of employees. The larger the company is, the bigger
the number of employees, and vice versa. It also has to do with the question number four
of the chapter IX. The staff: 4. How many employees does the company currently have by
the end of 2011? The number of SMEs respondent was 459 and each of them wrote the
number of employees in their own enterprise. There were some business which didn’t have
any answer related to this question, although the largest number of employees that has a
business was 157.
34
Experience of manager- This is also an independent variable. It deals with the years of
working experience of the manager has in the field of businesses activity. The question 13
is in the second chapter and respondents had to choose between three options: 1. I had
great working experience in the business I established, 2. I had little working experience in
the business I established, 3. I had no working experience in the business I established. The
total number of respondents was 464, where 256 of them had chosen the first option, 112
of them had chosen the second option and 96 of them had chosen the third option, while
the rest didn’t respond.
Education of manager- This is also an independent variable of the regression model. I
took this variable because most of enterprises in our country are small and are run by the
owner of the company, known as the manager. The question deals with the qualification of
manager. With regard to this question, 162 respondents had university degree, 285
respondents were without university degree, and the others did not respond. This is a
reflect that most of entrepreneurs are oriented towards business and a small number of
them possess a diploma degree. The question regarding this variable was in the chapter II
.18 Please, specify qualification and gender structure of founders? The respondents had to
choose between five options: 1. PhD, 2. Master, 3. Graduate, 4. Middle School, and 5.
Primary School.
This variable was also dummy variable where in the first group (appointed as 1) was for
respondents who have PhD, Master or Graduated, and in the second group (appointed as 0)
was for respondent with only primary and secondary education school.
Form of business- This was also a dummy independent variable. Form of business meant
the registered status of the business, grouping individual business (appointed as 1) in the
first group and otherwise (appointed as 0) including partnerships and joint stock companies
or corporation. The respondent had to choose between1.Individual businesses, 2.
Partnership and 3.Limited Liability Company/Corporation. The percentage of SMEs that
belonged to the first group, individual business, was 88.50%, otherwise SMEs that
decelerated themselves as partnership or Limited Liability Company was 10.06% and the
others that did not respond were 1.23 %.
Business Plan- The variable is part of the dummy independent variable of the regression
empirical model. Business plan is seen as very important asset of SMEs towards bankers,
because through it the business strategy can be described as well as on the way their
business activity would be realized, in order to convince bankers to lend the loan.
Entrepreneurs with reliable and convincing business plan find easier access to get credits
from the financial institutions. The question regarding this was presented in the chapter II,
question number 17: Currently, do you have written business plan? In the first group fell
those SMEs that got business plan (appointed as 1) and in the second were grouped SMEs
that had no business plan (appointed as 0). The number of respondents which decelerated
that have a business plan was 199, while the number of SMEs that had no business plan
were 279, and the rest did not respond.
35
Value of assets- This variable is also an independent variable of the regression model. I
considered this variable important because through it SMEs could state the total business
assets in the context of valuable collateral. It is considered that the more value of assets the
company has the easier access to a bank loan it will find, and vice versa because the
creditors will be secured in case of the client face difficulties to fulfill his obligation for
credit. The respondent has to write the amount in Euro per each company’s asset; e.g.
working capital, buildings, machine and equipment, transportation vehicle, land and other
assets in the current year.
6
EMPIRICAL FINDINGS
6.1
Empirical Consideration
This section elaborates empirical regression model and determines what features of
companies are impacting SMEs’ ability to get easier access to bank loans. The model has
been implemented on the statistical software named STATA, which mostly is used on
social science research for data analysis. STATA was created by STATA Corporation on
1985 and help researcher for their data management by implementing a very large number
of statistical techniques (SSCC, 2014).
Our variables are divided into two groups, the dependent variables (borrowed loan=1,
otherwise=0) and independent variables (gender, location, age of company, form of
business organization, experience of manager, education of manager, business plan, size of
company and value of assets).
Dependent variables with only two responses are called dummy variables or binary
responses, because they can give just two values (example: yes or no, success or failure) or
it gives a qualitative response rather than quantitative.
When dealing with dummy dependent variables, we should use Logit regression model
because if we use OLS it will be difficult to predict the purpose, error term is
heteroscedasticity and R-squared is useless. Logit model is used when the dependent
variables are dummy and the output of the variable should have a value 1 or 0 for success
or failure.
However, even the Probit Model can be useful with dummy dependent variables, giving
almost same result as given by Logistic model, except that Probit estimations are derived
from the cumulative normal distribution.
36
Figure 3. Logistic Regression
Pі
1
0
Xi
Source: University of Anger,Statistical software:Dummy dependent variables and nonlinear regression.
Therefore, the best empirical regression model that can fit to our variables is Logit
regression model. Logistic regression helps to determine dependent variables using one or
more independent variables, whether they are discrete, continuous, and dichotomous or any
combination of these (Fabowale et al., 1995). The main reason for using Logits is that in
many cases a linear model using probabilities does not fit the data, while the linear model
using Logits does (The Institute for Statistic Education). Thus, the Logit regression model
according to the Wooldridge (2006) is:
P(y =1 | x) = β₀+ β₁ x₁+ … +β x
Based on the model above, I have determined what feature of companies’ impact more on
SMEs getting easier access to bank loans. Thus, if y is equal to 1, it will be mean that
SMEs has taken loans from banks otherwise y will equal zero meaning that SMEs were
refused or didn’t apply for a loan.
The regression coefficient  ₀is called intercept from the linear regression equation
meaning the vector of independent variable in that model, β₁is called slope estimates and
measures the effect of Xі on Yі while X is an unknown parameter known as independent
variable (Sharyn &O’Halloran, 2005). By using the Logit model we will estimate the
probability of business characteristics having or not having an impact on bank decision to
lend credits or if it will impact their ability to get easier access to bank loans (Mramor &
Valentincic, 2002).
A number of independent variables and the residual were introduced in this equation, so
the calculation with those variables according to the empirical regression model is as
follows:
37
P( y = borrow_loan|X)= β₀ + β₁gender + β₂location + β₃ age_firm +
β₄ form_business + β₅ exp_man + β ₆ business_plan +
β ₇ education_man + β₈ size_firm + β₉ value_assets + µ
The error terms (µ) or residual is always introduced in the regression model in order to
capture all the other variables not included in the model (Kamaraz & Visser, 2012). The
higher the number of independent variables the value of residual or error terms will be
smaller.
Having into consideration that the Logit model can be used only for categorical or dummy
dependent variables by giving two odds of being a case based on the values of independent
variables, I have categorized the SMEs that got a loan in the first group y = 1, and y = 0 for
the other SMEs that couldn’t borrow loans from banks or were refused by bankers. This
classification was done, because when I did the regression for those that borrowed loans
and not applied, those that borrowed loans and were refused and those that borrowed loans
and other way around, the output result of the first and second group reveal nothing new
other than what is taken as output from the third applied regression model.
For interpretation of the results we will go through the average marginal effect and we will
also present the table of the Logit regression for all variables.
Based on the above, we have set out these hypotheses which were empirically tested:
Η₁: Male borrower entrepreneurs have higher probability to get bank loans compared to the
women entrepreneurs.
H₂: SMEs located in urban area have higher probability to get bank loans compared to
SMEs located in rural areas.
H₃: The older the SMEs the more bank loans SMEs can get and have higher probability to
get bank loans.
H₄: There is an association between form of business organization and probability of
getting easier access to bank loans.
H₅: Managers with more experience in their business can get more bank loans and have a
higher probability to get bank loans.
H₆: Those SMEs that have business plan have higher probability to get bank loans
compared to the SMEs that didn’t have.
38
H₇: Managers with better background in education can get more bank loans and have
higher probability to get bank loans.
H₈: The larger the size of SMEs, the more bank loans SMEs can get and it will have higher
probability to get bank loans.
H₉: The higher the value of assets, SMEs can get more loans and they will have higher
probability to get bank loans.
As we see there were taken nine hypothesis including firms characteristic such gender,
location, age of firm, size of firm, education of manager, value of assets, business plan,
form of business organization and experience of manager and were testified in order to
identify which of this characteristic are having impact on business ability to get access on
bank loan.
Before we go through the model and interpretation of the results we will do a T test
between the three groups to make sure if there is any significant differences on means of:
those that borrow loan and not applied, those that borrow loan and were refused and those
that borrow loan and otherwise.
39
Table 6. One Sample T-test Borrow Loan = 1 and Otherwise = 0
Variables
Number of
Ho mean=
95% Confidence interval of the
Ha mean=
T
difference
observation
Upper
Lower
Pr ( T < t)
Degree of
freedom
Pr ( |T| > |t| )
(df)
Pr (T > t)
Age_firm
465
9.25
.502
.497
.994
.006
464
Exp_man
464
1.65
.555
.444
.889
.139
463
Size_firm
459
8.84
.500
.499
.999
.001
458
Value_assets
486
397108
.500
.500
1.00
.000
485
Table 7. One Sample T-test Borrow Loan= 1 and Didn’t Apply =0
Variables
Number of
Ho mean=
95% Confidence interval of
Ha mean=
T
the difference
observation
freedom
Pr ( |T| > |t| )
Upper
(df)
Lower
Pr ( T < t)
Degree of
Pr (T > t)
Age_firm
453
9.30
.503
.496
.992
.009
452
Exp_man
452
1.65
.528
.472
.944
.070
451
Size_firm
447
8.79
.500
.499
.998
.002
446
Value_assets
475
405617.26
.500
.500
1.00
.000
474
40
Table 8. One Sample Ttest Borrow Loan =1 and Refused =0
Variables
Number of
Ho mean=
95% Confidence interval of the
Ha mean=
T
difference
observation
Upper
Pr ( T < t)
Lower
Degree of
freedom
Pr ( |T| > |t| )
(df)
Pr (T > t)
Age_firm
170
9.72
.497
.502
.995
-.005
169
Exp_man
168
1.68
.490
.509
.981
-.023
167
Size_firm
174
10.94
.500
.499
.998
.001
173
Value_assets
178
674993.3
.500
.500
1.00
.000
177
In the table above are presented the number of observations, mean of null hypothesis, the border of confidence interval of differences,
alternative hypothesis, t- which means the value of statistical test and degree of freedom for each continuous variable of the applied
regression model.
The results of the applied One Sample Ttest for the three of the groups those that borrow loan and otherwise, those that borrow loan and
were refused, and those that borrow loans and did not apply, has shown that there were no significant differences across three groups. As
it may be seen, the interpretation of the results from the applied regression Logit model of the SMEs who has got a loan in the first group
y = 1, and y = 0 or otherwise including those SMEs that were not able to borrow loans from banks or were refused by bankers, under
Institute
for
Digital
Research
and
Education
(hereinafter:
IDRE),
2013.
41
6.2
Interpretations of the results
In order to approximate the impact of firm features on their ability to get easier access to bank
loan we have used the empirical regression model through STATA 11 statistical software in order
to come to the proper result. STATA is a statistical package with smart data management
facilities which provide helpful and adequate statistical techniques to produce quality graphs. The
main goal of the STATA is to provide accurate result after the interpretation of the result
introduced on it.
The empirical regression model has been used to identify which factors or firm features are
having greater impact on SMEs ability to get easier access on bank loans. There were taken nine
firms features as variable and were classified as dependent and independent variable. In order to
get the research result we had used a dependent variable (borrow loan=1, otherwise=0) and the
other number of variables as independent (factors), than have assessed the impact of those
independent variables on the dependent variable.
However the model is composed from these variables: dependent variable (borrow loan) were
respondent has to choose between: Yes; No, didn’t try, and has been refused; and independent
variable .The independent variable used on the regression model were: Gender were the
respondent has to choose between male or female, location of business- were the respondent has
to choose if their business is located in urban or rural areas, age of company- were the firms has
to show starting year of their operation, form of business organization-were businesses were able
to choose based on their legal status individual business or otherwise (partnership or society
limited liability), business plan-yes if they had and no for otherwise, experience of managerthere should choose between three option I had great experience in the business which I had
openI had little experience in the business which I had open I had no experience at all in the
business that I have open, education of manager- there should select the answer by expressing
their education such as, primary school, high school and bachelor, MSc or PhD, size of company
which has been measured through the number of employee in the business and value of assetswere business were able to present all their assets and their value).
Those variables were introduced to the STATA and were used appropriate measurement methods
which has lead us to the interpretation of the results. Than each independent variable has been
compare with the dependent variables and has given us the results which describe the effect of
the used independent variable along to our dependent variable.
The interpretation of results of these variables were taken after the calculation of average
marginal effect on the empirical regression model. Average marginal effect gives the exact
approximation of measuring the dependent variable y that will be produced if the regression
42
will change for one unit from the average. The main advantages of using this compared to the
linear probability model or (LPM) is that it gives a single number that expresses the effect of
variable on P (y=1)(Long 1997; Long & Freese, 2003 & 2006, Cameron & Trivedi’s, 2013).
Cameron and Trivedi (2013, p. 333) argue that marginal effect or partial effect measures the
effect on the conditional mean of y of a change in one of the regressors
, thus equals the
relevant slope coefficient and gives greatly simplified analysis. It produces useful and
understandable results even in cases when there is categorical independent variable by showing
how P (y=1) changes as the categorical variable changes from 0 to 1. While for the continues
independent variables the average marginal effect measures the instantaneous change (Long &
Fresse, 2003). In fact these margin commands produce easily the result and accurate result
compare to the use of the marginal effect because all of the data is being used not just means.
Thus, through the average marginal effect model we had identified which of the firm features are
influencing more to get easier access on bank loans by elaborating each one of them. The average
marginal effect is used by calculating the focal variable while holding at their constant all other
variables (Arzheimer, 2013). Baum (2010, p. 13) also has cited on his study regarding the uses of
factors variable that the default behavior of margins is calculated through the average marginal
effect rather than marginal effect at the average or at some other point in the space of the
regressors. Also there seems to be different practices which favors the use of the average
marginal effect by computing each observation’s marginal effect with respect to any explanatory
factor, averaged over the estimation sample to the computation of the marginal effect at the
average (Baum, 2010).
In order to have more accurate result except the use of average marginal effect model for
interpretation of the result we had calculate also the result through the Logit model and have
compared which each other.
From the table below we can see that most of the variables have given the same result calculated
through the average marginal effect as well on Logit model. At the first column of the table are
ranked all the independent variable of the model known as the business characteristic, at the
second column it is presented the average marginal effect pointing out the significant variables
while on the third column of the table it is presented the Logit regression model with the all
implemented variable calculated through that model. According to the average marginal effect,
the variable has been interpreted, as shown in the table below:
43
Table 9. Empirical Findings from Average Marginal Effect and Logit Model
Average Marginal Effect
Variables
Gender
Dy/dx
P> |z|
Logit model
Sig
.059
.441
Location
-.151
.062
Age_firm
.008
.044
- .041
Experience_ man
Coefficients
P> |z|
Sig
.270
.427
-.661
.050
**
.041
.044
***
.642
-.188
.634
.032
.308
.153
.308
Business_plan
.087
.101
.409
.099
Education_manager
.049
.353
.232
.348
Size_firm
.001
.090
.008
.090
8.07e-09
.244
3.80e-08
.243
Form_business
Value_assets
**
***
*
**
Note.*** Significant in 1% (level of significance 99%) ** Significant in 5% (level of significance 95%)* Significant in 10% (level of significance 90%).
44
*
**
Gender- is not proved to be an important factor of the empirical findings, because as it may be
seen, there is no element that suggests that a male or a female is more favorable or has more
probability to borrow loan from banks or any credit financial institution. Therefore it means
that loan official (credit official) makes no differences or disparity and treat the same both
female or male credit borrowers.
Location- seems to be significant factor and it does impact the ability of SMEs to get easier
access to bank loans. According to the results, a company in a rural area has more probability
to get access to bank loan compared to the company located in an urban area. Consequently,
firms located in rural area have .15 times more probabilities to get easier bank loan at 5% level
of significance. This may be due to the small number of companies that operate in rural areas
compared to the high number of companies operating in urban areas. In contrary to our results,
OECD (2008) in the study regarding Scottish SMEs, found that businesses located in urban
area face less restriction to access sources of finance, compared to a business located in rural
areas because there were evidences that in rural location banks used to deal with only certain
sectors(Deakins, North, Baldock & Whittam, 2008).
Age of companies- is significant at 1% percentage level of significance and it does have
positive impact on getting easier access to bank loan. An increase of the year of company’s
operation starting from the average point increase the probability of company to get easier
access to bank loan by 0.01. This result shows that an older company has more chances to get
bank loans compared to newer company. An older company operating in markets has more
valuable assets, longer credit history and thus becomes more trustful for the bankers compared
to the newer or start up company. Thus, if the age of the company increases by one year, then
the probability of company to borrow a loan from banks increases by 0.01. Same as our result
Sun Xuegong and Liu Xueyan (2011, p. 358) through their Logit model regarding their study
of SMEs access to finance in China found that the older the firm it is the easier access they
will have to get bank loans compared to the newer business.
From the empirical findings we have seen also that the form of business organization is
found not to be significant. Whether SMEs is an individual business or otherwise (partnership
or Limited Liability Company) has no impact on their capability to get easier loan from banks
institution.
Experience of manager- cannot be considered as an important factor that may influence on
bank lending activity. These come as results for those companies that operate with low
productivity, low financial capacity, worthless assets in market, inappropriate collateral to put
as guarantee for banks etc. Thus, even if the manager has enough experience and good
45
background in education, this can’t improve more or less the probability of the company to
borrow from banks for the companies characterized with the features mentioned above.
However, the other variable business plan is found to be quite significant. According to our
results, SMEs that have prepared good business plan have higher probability to find easier
access to bank loans compared to the companies that do not have business plan. Thus, if
business provides promising and well prepared business plan for creditors than the probability
of getting the loan will rise for .08 at 10% in level of significance. Same as in our results,
Abdesamed and Wahab (2012) in their study about 76 SMEs in Tripoli, capital of Libya,
found that business plan is playing an important role for Start-up business in borrowing,
during assessment by bankers.
Education of manager- does not seem to be a significant factor that can influence on the
probability of businesses to get access to bank credits in our country. Thus, being with a good
background of qualification manager does not have higher probability to find easier access to
bank loan compare to the manager characterized with less background of manager
qualification. However, several studies have mentioned the important role of having a welleducated manager. Irwin and Scott (2005, p. 8), based on their research, found out that
education plays an important role on bank’s lending decision, because entrepreneurs with
better education background have better chance to borrow from banks.
Size of companies- is another variable taken into consideration in the regression model, and is
found to be significant at level five in significance percentage and it has positive impact on
SMEs’ access to bank loans. As the number of employee’s increases, starting from the
average, the probability of the company to get bank credit increases by 0.002 at 5% level of
significance. The positive sign of the variable means that larger firms have higher probability
to borrow loans from banks compared to the smaller ones. Thus, companies or SMEs having
higher number of employees will find easier access to bank loans compared to the ones with
smaller number of employees. Fatkoi and Asah (2011, p. 174) in their survey through logistic
regression found that SMEs with a number of employees over 50 find easier access to bank
loans compared to the ones that have less than 50 employees.
Although the variable value of assets or collateral has a significant role in the ability of
companies to borrow from banks, the result of our data revealed this variable to be
insignificant, thus I suggest that there should be other studies that have to measure it. Most of
the banks use as the main security for guarantees the borrower’s collateral. Phuong (2012,
p.103) in his study: What determines the access to credit by SMEs? Using World Bank
Enterprise Survey (2009) found that the value of assets for collateral play a crucial role for
businesses that want to borrow loans from creditors also in Vietnam.
46
Thus, the results of used empirical regression model with SMEs data in Kosovo shows that:
age of company, location of business, size of company and business plan does impacting
banks’ decision to lend credits. However, other obstacles such as experience of the manager,
education of the manager, gender of business owner, value of assets and form of business
organization doesn’t have any impact in the banks’ decision to lend.
POLICY RECOMMENDATIONS
Thus, based on the result taken from the applied probabilistic Logit model from which were
identified the most influencing characteristics of SMEs on bank management decision to
borrow loan, we have reached out some recommendation:

Enterprenerus or SMEs if they need a funds before going to ask for bank loan they
should prepare a houpful and persuasive business plan for banks management. They should
present in detail their strategic goal, reliable financial stattementand their idea of business
development in order to be more convincing on bank management decision. Moreover, even if
a start up business makes a visionary and persuasuve business planwill increase their ability of
finding easier access to bank loan.

Banks should provide facilitate by their activity of lending more to business located
and operating to the urban areas because number of business is much more higher operating in
urban area comparing to the business located in rural.

Business should not hesitate to ask for bank loans. Even they are start up business or
just with couple years of operating in the market having a few number of employees and less
valuable of assets compare to the larger company they must apply for bank loan and persuade
the bankers that their business idea is valuable and needs to continue their operation.

Bankers hesitate to lend due to the imperfection market and inefficent juridical system.
They are keeping the highest interest rate on loan in the region countries for safety reason
because if the borrower fail to pay their debts the banks will have to collect non performing
loans. Thus banks will have to lower interest rate on credits and businesses or SMEs will raise
their investment, number of employment and the whole economy.
CONCLUSION
The aim of this master thesis was identification of the obstacles faced by SMEs in accessing to
finance sources for their activity and business operations. In order to identify these obstacles, a
47
number of business features, that we thought were relevant, were taken into account and
analyzed to determine whether they have an impact on SMEs’ access to bank loans.
The research has been conducted by using the data taken from 488 SMEs located in seven
municipalities of Kosovo including urban and rural areas. From the collected data we made
descriptive statistical analysis, T-test between three groups to make sure if there is any
significant differences in terms of: those that borrow loan and not applied, those that borrow
loan and were refused and those that borrow loan and other way around, and empirical model
through which the main characteristic were identified that are impacting SMEs ability to get
access on bank loans.
Micro, small and medium enterprises in Kosovo are facing different obstacles in their growth
and development of their operations. Growth of SMEs is strongly dependent on finding
financial sources to continue or expand their businesses. Most of them start their business by
financing their operation with own funds, or funds borrowed from their friends, family or
remittances. After using these financial sources they try to get external sources of finance from
banks, microfinance institution etc. The only sources of external finances used by SMEs
operating in Kosovo are loans. Being the only source of financing, banks are having a decisive
role in financing business operations, however they impose some conditions that companies
have to meet in order to be able to get the loan. Banks, in their lending activities in recent
years, are being very strict towards companies and have also tightened the lending activities
due to the market risks. They require from businesses a number of criteria to be fulfilled if
they want to borrow loans from banks. Such criteria include preparation of promising business
plan, reliable financial statement of the company, guarantor, collateral etc.
This happens mainly due to high interest rates of loans, collateral requirement, application
costs and other restrictions imposed by banks. Due to these barriers imposed by banks for
borrowers, there are a number of companies that do not even go to banks to ask for a bank
loan and as result they fail to implement their business operations. As it can be seen, there are
also a potential number of companies who may have good idea for development of business,
but are discouraged due to difficult conditions imposed by banks. Usually, parts of this group
are micro or start-up businesses which at early years of operation face disadvantages due to
their value of assets, internal funds, asymmetry information etc.
Analyzing each variable through the empirical regression model we have reached some
conclusion regarding which businesses have advantage in front of bankers to borrow loan.
Different SME characteristics that may have impact on banks’ lending decision were taken
into consideration. The empirical findings suggest that a number of variables which are found
to be significant in developed countries such as value of assets, education of manager and
48
experience of manager are found to be not significant in Kosovo. The results from the
empirical model have shown that older firms in Kosovo, with larger number of employees,
prepared persuasive business plans and the ones located in rural areas find it easier to get
access to bank loans. While the other results of variables taken from the applied Logit
regression model, such as experience of manager, gender, education of manager, value of
assets and form of business organization are found to be not important during banks’ decision
making about lending.
The results taken from the applied regression model also suggest that access to finance is not
only dependent on the banks, but it is also dependent on the businesses because they should
pay more attention on preparation of promising business plan and give accurate information to
banks if they want to borrow credits from them. Special role in our country is played recently
by Micro Finance Institutions as they are providing facilitations for small business and poor
entrepreneurs as well as people living with low incomes to get easier access to credits.
49
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APPENDICES
LIST OF APPENDICES
Appendix A: List of Abbreviations……………………………………………………………..2
Appendix A: Ttest (Borrow loan and otherwise)…………………………………………....…3
Appendix B: Ttest (Borrow loan and refused)….………………………………………..…….5
Appendix C: Ttest (Borrow loan and didn’t apply)…………………………………….………7
Appendix D:LogitRegresion Model L(Borrow loan=1 and Otherwise=0)-(G, L, AF, FB, ExM,
BP, EM, S, V, μ)................................................................................................................8
Appendix E: Logit Regression Model L(Borrow loan=1 and didn’t apply=0)-(G, L, AF, FB,
ExM, BP, EM, S, V, μ)................................................................................................................9
Appendix F: Logit Regression Model L(Borrow loan=1 and refused=0)-(G, L, AF, FB, ExM,
BP, EM, S, V, μ)........................................................................................................................10
Appendix G: Questionnaires of SMEs…………….………………..…………………….…..12
1
List of Abbreviations
AMIK- Association of Microfinance Institution of Kosovo
BAK- Bank Association of Kosovo
BPA-Banking Payment Authority
BSCK- Business Support Center of Kosovo
BTI -Bertelsmann Stiftung’s Transformation Index
CBK- Central Bank of Kosovo
EC- European Commission
EU- European Union
FDI- Foreign direct Investment
FIEG-Financial Inclusion Expert Group
IAS-Insurance Association of Kosovo
IFC-International Finance Corporation
IMF- International monetary found
IPC -International Project Consult
KBRA- Kosovo Business registration administrate
KAS- Kosovo Agency of Statistics
MTI- Ministry of Trade and Industry
MF- Ministry of Finance
NPL- Non performing Loan
OECD-Organization for Economic Co operation and Development
SME- Small and Medium Enterprise
TAK- Tax administration of Kosovo
WB- World Bank
STATA- Statistical software
2
Appendix A: Table One Sample T-test
Borrow Loan =1 and Otherwise =0
. ttestage_firm == 9.25
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------age_firm |
465
9.251613
.2526607
5.448339
8.755112
9.748114
-----------------------------------------------------------------------------mean = mean(age_firm)
t =
Ho: mean = 9.25
0.0064
degrees of freedom =
Ha: mean < 9.25
Ha: mean != 9.25
Pr(T < t) = 0.5025
464
Ha: mean > 9.25
Pr(|T| > |t|) = 0.9949
Pr(T > t) = 0.4975
. ttestexp_man == 1.65
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------exp_man |
464
1.655172
.0371709
.8006849
1.582128
1.728217
-----------------------------------------------------------------------------mean = mean(exp_man)
t =
Ho: mean = 1.65
Ha: mean < 1.65
Pr(T < t) = 0.5553
0.1392
degrees of freedom =
Ha: mean != 1.65
Pr(|T| > |t|) = 0.8894
. ttestsize_firm == 8.84
3
463
Ha: mean > 1.65
Pr(T > t) = 0.4447
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------size_f~m |
459
8.840959
.9698713
20.7788
6.935009
10.74691
-----------------------------------------------------------------------------mean = mean(size_firm)
t =
Ho: mean = 8.84
0.0010
degrees of freedom =
Ha: mean < 8.84
Ha: mean != 8.84
Pr(T < t) = 0.5004
458
Ha: mean > 8.84
Pr(|T| > |t|) = 0.9992
Pr(T > t) = 0.4996
. ttestvalue_assets == 397108.43
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------value_~s |
486
397108.4
179711.1
3961804
44000.02
750216.8
-----------------------------------------------------------------------------mean = mean(value_assets)
t =
Ho: mean = 397108
Ha: mean < 397108
Pr(T < t) = 0.5000
0.0000
degrees of freedom =
Ha: mean != 397108
Pr(|T| > |t|) = 1.0000
4
485
Ha: mean > 397108
Pr(T > t) = 0.5000
Appendix B: Table One Sample T-test
Borrow loan =1 and Not applied =0
. ttestage_firm == 9.30
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------age_firm |
453
9.302428
.2567874
5.465412
8.797783
9.807074
-----------------------------------------------------------------------------mean = mean(age_firm)
t =
Ho: mean = 9.30
0.0095
degrees of freedom =
Ha: mean < 9.30
Ha: mean != 9.30
Pr(T < t) = 0.5038
452
Ha: mean > 9.30
Pr(|T| > |t|) = 0.9925
Pr(T > t) = 0.4962
. ttestexp_man == 1.65
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------exp_man |
452
1.652655
.0377477
.8025276
1.578472
1.726838
-----------------------------------------------------------------------------mean = mean(exp_man)
t =
Ho: mean = 1.65
Ha: mean < 1.65
Pr(T < t) = 0.5280
0.0703
degrees of freedom =
Ha: mean != 1.65
Pr(|T| > |t|) = 0.9440
. ttestsize_firm == 8.79
5
451
Ha: mean > 1.65
Pr(T > t) = 0.4720
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------size_f~m |
447
8.791946
.9865816
20.85868
6.85302
10.73087
-----------------------------------------------------------------------------mean = mean(size_firm)
t =
Ho: mean = 8.79
0.0020
degrees of freedom =
Ha: mean < 8.79
Ha: mean != 8.79
Pr(T < t) = 0.5008
446
Ha: mean > 8.79
Pr(|T| > |t|) = 0.9984
Pr(T > t) = 0.4992
. ttestvalue_assets == 405617.26
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------value_~s |
475
405617.3
183858.1
4007094
44339.55
766895
-----------------------------------------------------------------------------mean = mean(value_assets)
t =
Ho: mean = 405617
Ha: mean < 405617
Pr(T < t) = 0.5000
0.0000
degrees of freedom =
Ha: mean != 405617
Pr(|T| > |t|) = 1.0000
6
474
Ha: mean > 405617
Pr(T > t) = 0.5000
Appendix C: Table One Sample T-test
Borrow Loan= 1 and Refused -=0
. ttestage_firm == 9.72
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------age_firm |
170
9.717647
.4303346
5.610877
8.868123
10.56717
-----------------------------------------------------------------------------mean = mean(age_firm)
t =
Ho: mean = 9.72
-0.0055
degrees of freedom =
Ha: mean < 9.72
Ha: mean != 9.72
Pr(T < t) = 0.4978
169
Ha: mean > 9.72
Pr(|T| > |t|) = 0.9956
Pr(T > t) = 0.5022
. ttestexp_man == 1.68
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------exp_man |
168
1.678571
.0622283
.8065705
1.555716
1.801427
-----------------------------------------------------------------------------mean = mean(exp_man)
t =
Ho: mean = 1.68
Ha: mean < 1.68
Pr(T < t) = 0.4909
-0.0230
degrees of freedom =
Ha: mean != 1.68
Pr(|T| > |t|) = 0.9817
7
167
Ha: mean > 1.68
Pr(T > t) = 0.5091
. ttestsize_firm == 10.94
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------size_f~m |
174
10.94253
1.876347
24.75071
7.239049
14.64601
-----------------------------------------------------------------------------mean = mean(size_firm)
t =
Ho: mean = 10.94
0.0013
degrees of freedom =
Ha: mean < 10.94
Ha: mean != 10.94
Pr(T < t) = 0.5005
173
Ha: mean > 10.94
Pr(|T| > |t|) = 0.9989
Pr(T > t) = 0.4995
. ttestvalue_assets == 674993.29
One-sample t test
-----------------------------------------------------------------------------Variable |
Obs
Mean
Std. Err.
Std. Dev.
[95% Conf. Interval]
---------+-------------------------------------------------------------------value_~s |
178
674993.3
458661.3
6119305
-230155.1
1580142
-----------------------------------------------------------------------------mean = mean(value_assets)
t =
Ho: mean = 674993
Ha: mean < 674993
Pr(T < t) = 0.5000
0.0000
degrees of freedom =
Ha: mean != 674993
Pr(|T| > |t|) = 1.0000
8
177
Ha: mean > 674993
Pr(T > t) = 0.5000
Appendix D: Logit Regression Model
L (Borrow loan=1, otherwise =0)-(G, L, AF, FB, ExM, BP, EM, S, V, μ)
. (10 vars, 487 obs pasted into editor)
logit loan gender location
age_firmform_businessexp_manbusiness_planedu_mansize_firmvalue_assets
Iteration 0:
log likelihood = -223.98371
Iteration 1:
log likelihood = -214.38851
Iteration 2:
log likelihood = -214.32461
Iteration 3:
log likelihood =
-214.3246
Logistic regression
Prob> chi2
=
0.0226
Log likelihood =
-214.3246
Number of obs
=
360
LR chi2(9)
=
19.32
Pseudo R2
=
0.0431
-----------------------------------------------------------------------------loan |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gender |
.2697734
.3397838
0.79
0.427
-.3961906
.9357375
location |
-.6612743
.3375149
-1.96
0.050
-1.322791
.0002427
age_firm |
.0418361
.0207999
2.01
0.044
.0010691
.0826032
form_busin~s|
exp_man |
-.1881582
.1531019
business_p~n |
edu_man |
size_firm |
-0.48
.1502211
.4087818
.2323423
.3946523
.2474464
.2475894
.0081522
1.02
0.308
1.65
0.94
.004806
0.634
-.1413261
0.099
0.348
1.70
-.9616624
.4475299
-.0762043
-.252924
0.090
.585346
.8937679
.7176085
-.0012675
.0175718
value_assets |
3.80e-08
3.26e-08
1.17
0.243
-2.58e-08
1.02e-07
_cons |
-1.089745
.6487751
-1.68
0.093
-2.361321
.1818307
9
Appendix E: LogitRegression Model
L (Borrow loan=1 and Didn’t apply=0)-(G, L, AF, FB, ExM, BP, EM, S, V, μ)
(10 vars, 444 obs pasted into editor)
logit loan gender location
age_firmform_businessexp_manbusiness_planedu_mansize_firmvalue_assets
Iteration 0:
log likelihood = -220.15153
Iteration 1:
log likelihood = -211.02094
Iteration 2:
log likelihood = -210.96697
Iteration 3:
log likelihood = -210.96697
Logistic regression
Prob> chi2
=
Number of obs
=
350
LR chi2(9)
=
18.37
Pseudo R2
=
0.0417
0.0311
Log likelihood = -210.96697
-----------------------------------------------------------------------------loan |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gender |
.2949499
.3428051
0.86
0.390
-.3769358
.9668356
location |
-.6943775
.3418848
-2.03
0.042
-1.364459
-.0242955
age_firm |
.039264
.0208284
1.89
0.059
-.001559
.0800869
form_busin~s|
exp_man |
-.1566394
.1638347
business_p~n |
edu_man |
size_firm |
.150675
.3664847
.2227382
.3947062
1.09
.2481192
.248355
.0081449
-0.40
0.277
1.48
0.90
.004836
0.691
-.1314828
0.140
0.370
1.68
-.9302494
0.092
.4591523
-.1198201
-.2640286
.6169706
.8527895
.7095049
-.0013336
.0176234
value_assets |
3.71e-08
3.21e-08
1.16
0.248
-2.58e-08
1.00e-07
_cons |
-1.024803
.6518969
-1.57
0.116
-2.302497
.2528918
------------------------------------------------------------------------------
10
Appendix F: LogitRegression Model
L (Borrow loan=1 and refused=0)- (G, L, AF, FB, ExM, BP, EM, S, V, μ)
. (10 vars, 148 obs pasted into editor)
logit loan gender location
age_firmform_businessexp_manbusiness_planedu_mansize_firmvalue_assets
note: form_business != 1 predicts success perfectly
form_business dropped and 13 obs not used
Iteration 0:
log likelihood = -33.509971
Iteration 1:
log likelihood = -30.270796
Iteration 2:
log likelihood = -29.739387
Iteration 3:
log likelihood =
Iteration 4:
log likelihood = -29.549997
Iteration 5:
log likelihood = -29.317543
Iteration 6:
log likelihood =
Iteration 7:
log likelihood = -28.959396
Iteration 8:
log likelihood = -28.942209
Iteration 9:
log likelihood = -28.941768
Iteration 10:
log likelihood = -28.941768
-29.67305
-29.06883
Logistic regression
Prob> chi2
=
Number of obs
=
110
LR chi2(8)
=
9.14
Pseudo R2
=
0.1363
0.3309
Log likelihood = -28.941768
-----------------------------------------------------------------------------loan |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------gender |
-.3261714
location |
.075419
.9003862
.8985633
-0.36
0.08
0.717
0.933
11
-2.090896
-1.685733
1.438553
1.836571
age_firm |
exp_man|
.0708871
-.0284366
business_p~n |
edu_man |
size_firm |
.0778192
.4572041
1.420813
.5325248
0.362
-0.06
1.132946
.865015
.0021541
0.91
0.950
1.25
0.62
.014997
-.9245402
0.210
0.538
0.14
-.0816358
.867667
-.799721
-1.162873
0.886
.22341
3.641346
2.227923
-.0272396
.0315477
value_assets |
4.15e-06
5.11e-06
0.81
0.417
-5.87e-06
.0000142
_cons |
.9375146
1.353411
0.69
0.488
-1.715122
3.590151
-----------------------------------------------------------------------------Note: 0 failures and 2 successes completely determined.
12
Appendix G: Questionnaires for SMEs
1. Gender:
1.
1. Female
2. Male
Enterprise operate on (circle):
1. Urban
2. Rural
2. 4. Year of establishement (writte the year when etnerprise has began the work):
_________________.
6.Your enterprise is (circle):
1)
Individual business
2)
3)
Partnership
Joint stoct- corporate
Did you have experience in the field were you started your business?
1.
I had great experience in the business which I had open
2.
I had little experience in the business which I had open
3.
I had no experience at all in the business that I have open
4. 16. Actualy do you have business plan?
1. YES
2. NO
5. IV. Barriers/ Barriers on doing business
6. 1. According to your opinion rank factors that represent obstacle on doing
business ?
7. 5=very large barrier, 4= major barrier, 3= is barrier, 2= minor barrier, 1= no
barrier), write the numbers in the following sentences:
No
Description
1
High tax
2
Administrative charge of tax
3
Adequate and sufficient laws in force
4
Legislation and their implementation
5
Harmful/ strong competition
6
Corruption
7
Fiscal evasion
1
13
2
3
4
5
9(N/A)
8
Crime, burglary and anarchy
9
In formal and black economy
10
Access to finance(lack of external source of
financing)
11
Insufficient capacity
12
Political instability
13
Your managerial skills
14
Permits and licensing for businesses
15
Inadequate level of employee skills
16
Transportation
17
Electricity supply
18
Provide material, machines and equipment
19
Lack of market demand
20
Delaying payment (debt collection )
21
Lack of informationregarding to businesses
Other (specify)______________________
Do you have borrow loan from banks?
YES (if YES go to question 7)
NO. I haven’t tried? ( go to questions 13)
NO. I tried, but I’ve been refused? ( go to questions 14)
If you have borrow loan, please provide the following information for the last loan you
borrow:
What was the total amount of loan ______________________(€)
Is secret
When? ( year) ______________________
What was the period of loan repayment? (in month) ___________
14
How much was the interest rate (in %) _______________
Is it needed collateral to obtain loan?
1. YES
2. NO
If YES what is used as collateral? ________________________________
Mines or family real estate
Company real estate
Something else ____________________ ( specify what)
What was the total value of collateral?____________________ (Euro).
If you have circle questions 6.2 (No. I haven’t tried to borrow loan) the reason was:
I haven’t need – enterprise had sufficient capital
Application procedures has been quite complex
High interest rate
Requirement for collateral has been high
Duration of loan return hasn’t been sufficient
I didn’t know how to apply for loan
I was not sure that they will gave me the loan
Other (please specify) _______________________
If you have circle questions 6.3 (NO. I tried but I ws refused) the reason was (circle
all relevant option)
Lack of collateral
Lack of business plan
Lack of documents required by the bank
Other (please specify) _________________.
If you borrow loan, terms of loan have been (5=very unfavorable and 1= favorable):
________________.
During 2011, which have been the main source of financing your working capital
(stock, short term payment)
1. Personal saving
_____________%
2. Retained earning
_____________%
3. Borrow from family or friends
4. Loan from bank
_____________%
_____________%
15
5. Loans from special programs to support SMEs
6. Loans from informal equity market
7. Loans from domestic supplier
8. Loans from foreign supplier
9. Late payment of taxes and contribution
10. Other (please specify)
16
_____________%
_____________%
_____________%
_____________%
_____________%
_____________%